Add model selection

Add gpu selection
Add gpu provisioning
2026-02-14 02:06:09 +00:00 · 2026-02-05 04:02:49 +00:00 · 2026-02-04 07:39:31 +00:00 · 2026-02-02 21:36:52 +00:00 · 2026-02-01 00:12:07 +00:00
149 changed files with 9484 additions and 6930 deletions
@@ -40,6 +40,9 @@ dist/
 *.egg
 eggs/
 .eggs/
+node_modules/
+.vite
+vite.config.js.timestamp-*.mjs

 # MkDocs documentation
 site/
@@ -1,29 +1,35 @@
+<div align="center">
+<img src=assets/logos/logo.svg width="30%"/>
+</div>

-| **[Documentation](https://hao-ai-lab.github.io/FastVideo)** | **[Quick Start](https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/)** | **[Weekly Dev Meeting](https://github.com/hao-ai-lab/FastVideo/discussions/982)** | 🟣💬 **[Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ)** |
+<p align="center">
+     | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/"><b> Quick Start</b></a> | <a href="https://github.com/hao-ai-lab/FastVideo/discussions/982"  target="_blank"><b>Weekly Dev Meeting</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/sv3MMKyv" target="_blank"> <b> WeChat </b> </a> |
+</p>

 **FastVideo is a unified post-training and inference framework for accelerated video generation.**

 ## NEWS
+- ```2025/11/19```: Release [CausalWan2.2 I2V A14B Preview](https://huggingface.co/FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers) models, [Blog](https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/) and [Inference Code!](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py)
+- ```2025/08/04```: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).

- `2025/11/19`: Release [CausalWan2.2 I2V A14B Preview](https://huggingface.co/FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers) models, [Blog](https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/) and [Inference Code!](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py)
- `2025/08/04`: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
+<details>
+<summary>More</summary>

-### More News
+- ```2025/06/14```: Release finetuning and inference code for [VSA](https://arxiv.org/pdf/2505.13389)
+- ```2025/04/24```: [FastVideo V1](https://hao-ai-lab.github.io/blogs/fastvideo/) is released!
+- ```2025/02/18```: Release the inference code for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/).

- `2025/06/14`: Release finetuning and inference code for [VSA](https://arxiv.org/pdf/2505.13389)
- `2025/04/24`: [FastVideo V1](https://hao-ai-lab.github.io/blogs/fastvideo/) is released!
- `2025/02/18`: Release the inference code for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/).
+</details>

 ## Key Features

 FastVideo has the following features:
-
 - End-to-end post-training support for bidirectional and autoregressive models:
  - Support full finetuning and LoRA finetuning for state-of-the-art open video DiTs
  - Data preprocessing pipeline for video, image, and text data
  - Distribution Matching Distillation (DMD2) stepwise distillation.
  - Sparse attention with [Video Sparse Attention](https://arxiv.org/pdf/2505.13389)
-  - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) to achieve >50x denoising speedup
+  - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) to achineve >50x denoising speedup
  - Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing.
  - Causal distillation through Self-Forcing
  - See this [page](https://hao-ai-lab.github.io/FastVideo/training/overview/) for full list of supported models and recipes.
@@ -38,7 +44,6 @@ FastVideo has the following features:
  - See this [page](https://hao-ai-lab.github.io/FastVideo/inference/hardware_support/) for full list of supported hardware and OS.

 ## Getting Started
-
 We recommend using an environment manager such as `Conda` to create a clean environment:

 ```bash
@@ -53,20 +58,18 @@ pip install fastvideo
 Please see our [docs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/) for more detailed installation instructions.

 ## Sparse Distillation
-
 For our sparse distillation techniques, please see our [distillation docs](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/) and check out our [blog](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).

 See below for recipes and datasets:

-| Model                                                                                 | Sparse Distillation                                                                                             | Dataset                                                                                                  |
-| ------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- |
-| [FastWan2.1-T2V-1.3B](https://huggingface.co/FastVideo/FastWan2.1-T2V-1.3B-Diffusers) | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P)       | [FastVideo Synthetic Wan2.1 480P](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x448x832_600k)     |
-| [FastWan2.2-TI2V-5B](https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-Diffusers)   | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free) | [FastVideo Synthetic Wan2.2 720P](https://huggingface.co/datasets/FastVideo/Wan2.2-Syn-121x704x1280_32k) |
+|                                            Model                                              |                                               Sparse Distillation                                                 |                                                  Dataset                                                  |
+|:-------------------------------------------------------------------------------------------:  |:---------------------------------------------------------------------------------------------------------------:  |:--------------------------------------------------------------------------------------------------------: |
+| [FastWan2.1-T2V-1.3B](https://huggingface.co/FastVideo/FastWan2.1-T2V-1.3B-Diffusers)         |    [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P)      | [FastVideo Synthetic Wan2.1 480P](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x448x832_600k)      |
+| [FastWan2.1-T2V-14B-Preview](https://huggingface.co/FastVideo/FastWan2.1-T2V-14B-Diffusers)   |                                                   Coming soon!                                                    |   [FastVideo Synthetic Wan2.1 720P](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x768x1280_250k)   |
+| [FastWan2.2-TI2V-5B](https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-Diffusers)           | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free)   | [FastVideo Synthetic Wan2.2 720P](https://huggingface.co/datasets/FastVideo/Wan2.2-Syn-121x704x1280_32k)  |

 ## Inference
-
 ### Generating Your First Video
-
 Here's a minimal example to generate a video using the default settings. Make sure VSA kernels are [installed](https://hao-ai-lab.github.io/FastVideo/video_sparse_attention/installation/). Create a file called `example.py` with the following code:

 ```python
@@ -105,36 +108,55 @@ python example.py

 For a more detailed guide, please see our [inference quick start](https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/).

-## More Guides
+### Other docs:

 - [Design Overview](https://hao-ai-lab.github.io/FastVideo/design/overview/)
+- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/)
+
+## Distillation and Finetuning
 - [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/)
- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/contributing/overview/)
+<!-- - [Finetuning Guide](https://hao-ai-lab.github.io/FastVideo/training/finetune.html) -->

 ## Awesome work using FastVideo or our research projects

- [SGLang](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen): SGLang's diffusion inference functionality is based on a fork of FastVideo on Sept. 24, 2025.
- [DanceGRPO](https://github.com/XueZeyue/DanceGRPO): A unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms. Code based on FastVideo.
- [SRPO](https://github.com/Tencent-Hunyuan/SRPO): A method to directly align the full diffusion trajectory with fine-grained human preference. Code based on FastVideo.
- [DCM](https://github.com/Vchitect/DCM): Dual-expert consistency model for efficient and high-quality video generation. Code based on FastVideo.
- [Hunyuan Video 1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5): A leading lightweight video generation model, where they proposed SSTA based on Sliding Tile Attention.
- [Kandinsky-5.0](https://github.com/kandinskylab/kandinsky-5): A family of diffusion models for video & image generation, where their NABLA attention includes a Sliding Tile Attention branch.
- [LongCat Video](https://github.com/meituan-longcat/LongCat-Video): A foundational video generation model with 13.6B parameters with block-sparse attention similar to Video Sparse Attention.
+- [SGLang](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen): SGLang's diffusion inference functionality is based  on a fork of FastVideo on Sept. 24, 2025. [![Star](https://img.shields.io/github/stars/sgl-project/sglang.svg?style=social&label=Star)](https://github.com/sgl-project/sglang)
+
+- [DanceGRPO](https://github.com/XueZeyue/DanceGRPO): A  unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/XueZeyue/DanceGRPO.svg?style=social&label=Star)](https://github.com/XueZeyue/DanceGRPO)
+- [SRPO](https://github.com/Tencent-Hunyuan/SRPO): A method to directly align the full diffusion trajectory with fine-grained human preference. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/Tencent-Hunyuan/SRPO.svg?style=social&label=Star)](https://github.com/Tencent-Hunyuan/SRPO)
+- [DCM](https://github.com/Vchitect/DCM): Dual-expert consistency model for efficient and high-quality video generation. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/Vchitect/DCM.svg?style=social&label=Star)](https://github.com/Vchitect/DCM)
+- [Hunyuan Video 1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5): A leading lightweight video generation model, where they proposed SSTA based on Sliding Tile Attention. [![Star](https://img.shields.io/github/stars/Tencent-Hunyuan/HunyuanVideo-1.5.svg?style=social&label=Star)](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5)
+- [Kandinsky-5.0](https://github.com/kandinskylab/kandinsky-5): A family of diffusion models for video & image generation, where their NABLA attention includes a Sliding Tile Attention branch. [![Star](https://img.shields.io/github/stars/kandinskylab/kandinsky-5.svg?style=social&label=Star)](https://github.com/kandinskylab/kandinsky-5)
+- [LongCat Video](https://github.com/meituan-longcat/LongCat-Video): A foundational video generation model with 13.6B parameters with block-sparse attention similar to Video Sparse Attention. [![Star](https://img.shields.io/github/stars/meituan-longcat/LongCat-Video.svg?style=social&label=Star)](https://github.com/meituan-longcat/LongCat-Video)

 ## 🤝 Contributing

 We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview/).
 See details in [development roadmap](https://github.com/hao-ai-lab/FastVideo/issues/899).
-
 ## Acknowledgement
+We learned and reused code from the following projects:
+- [Wan-Video](https://github.com/Wan-Video)
+- [ThunderKittens](https://github.com/HazyResearch/ThunderKittens)
+- [Triton](https://github.com/triton-lang/triton)
+- [DMD2](https://github.com/tianweiy/DMD2)
+- [diffusers](https://github.com/huggingface/diffusers)
+- [xDiT](https://github.com/xdit-project/xDiT)
+- [vLLM](https://github.com/vllm-project/vllm)
+- [SGLang](https://github.com/sgl-project/sglang)

-We learned the design and reused code from the following projects: [Wan-Video](https://github.com/Wan-Video), [ThunderKittens](https://github.com/HazyResearch/ThunderKittens), [DMD2](https://github.com/tianweiy/DMD2), [diffusers](https://github.com/huggingface/diffusers), [xDiT](https://github.com/xdit-project/xDiT), [vLLM](https://github.com/vllm-project/vllm), [SGLang](https://github.com/sgl-project/sglang). We thank [MBZUAI](https://ifm.mbzuai.ac.ae/), [Anyscale](https://www.anyscale.com/), and [GMI Cloud](https://www.gmicloud.ai/) for their support throughout this project.
+We thank [MBZUAI](https://ifm.mbzuai.ac.ae/), [Anyscale](https://www.anyscale.com/), and [GMI Cloud](https://www.gmicloud.ai/) for their support throughout this project.

 ## Citation
-
-If you find FastVideo useful, please consider citing our research work:
+If you find FastVideo useful, please considering citing our work:

 ```bibtex
+@software{fastvideo2024,
+  title        = {FastVideo: A Unified Framework for Accelerated Video Generation},
+  author       = {The FastVideo Team},
+  url          = {https://github.com/hao-ai-lab/FastVideo},
+  month        = apr,
+  year         = {2024},
+}
+
@article{zhang2025vsa,
  title={Vsa: Faster video diffusion with trainable sparse attention},
  author={Zhang, Peiyuan and Chen, Yongqi and Huang, Haofeng and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao},
@@ -43,7 +43,7 @@ RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
    uv pip install --no-cache-dir --upgrade pip && \
    uv pip install --no-cache-dir .[dev] && \
-    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp310-cp310-linux_x86_64.whl
+    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3%2Bcu128torch2.9-cp310-cp310-linux_x86_64.whl

 COPY . .

@@ -43,7 +43,7 @@ RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
    uv pip install --no-cache-dir --upgrade pip && \
    uv pip install --no-cache-dir .[dev] && \
-    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp311-cp311-linux_x86_64.whl
+    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3%2Bcu128torch2.9-cp311-cp311-linux_x86_64.whl

 COPY . .

@@ -43,7 +43,7 @@ RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
    uv pip install --no-cache-dir --upgrade pip && \
    uv pip install --no-cache-dir .[dev] && \
-    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp312-cp312-linux_x86_64.whl
+    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3%2Bcu128torch2.9-cp312-cp312-linux_x86_64.whl

 COPY . .

@@ -121,15 +121,6 @@ This page contains the complete API reference for the FastVideo library.
      show_root_toc_entry: true
      heading_level: 4

-## fastvideo.registry
-
-::: fastvideo.registry
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      heading_level: 3
-
 ## fastvideo.pipelines

 ::: fastvideo.pipelines
@@ -324,8 +324,8 @@ Purpose:
 Action:

 - Add a new pipeline config + sampling params.
- Register them in `fastvideo/registry.py` using explicit
-  `register_configs(...)` blocks (this file is the single source of truth now).
+- Register them in `fastvideo/configs/pipelines/registry.py` and
+  `fastvideo/configs/sample/registry.py`.

 ### 5) Wire pipeline stages

@@ -17,7 +17,7 @@ You can easily use the FastVideo Pod Template on [RunPod](https://www.runpod.io)
 - Select the "FastVideo" or "fastvideo-dev" Pod Template.
 ![RunPod Pod Template Selection](../../assets/images/runpod_create.png)

- Set the Pod name to "`<name>-<FastVideo>-<date>`".
+- Set the Pod name to "<name>-<FastVideo>-<date>".

 - Finally, once the pod is deployed (will take a few minutes as the image is being pulled), you can SSH into it using "SSH exposed over TCP". You'll need to use the matching private ssh key you provided.
 ![RunPod SSH](../../assets/images/runpod_ssh.png)
@@ -51,9 +51,8 @@ runtime parameters consistent:
 - `fastvideo/configs/pipelines/`: pipeline wiring and required components.
 - `fastvideo/configs/sample/`: default sampling parameters (steps, frames,
  guidance scale, resolution, fps).
- `fastvideo/registry.py`: unified registry for pipeline config + sampling
-  defaults and model metadata resolution, defined via explicit
-  `register_configs(...)` blocks (no separate dict registries).
+- `fastvideo/configs/registry.py`: pipeline registry.
+- `fastvideo/configs/sample/registry.py`: sampling param registry.

 `FastVideoArgs` (in `fastvideo/fastvideo_args.py`) provides runtime settings and
 is passed into pipeline construction and stages.
@@ -131,41 +131,17 @@ self.attn = DistributedAttention(

 ### Registering Models

-Register implemented modules for auto‑discovery by adding `EntryClass` in each
-model module (the registry scans for it):
+Register implemented modules in the model registry:

 ```python
-# In fastvideo/models/dits/your_module.py
-class YourTransformerModel(...):
-    ...
+# In fastvideo/models/registry.py
+_TEXT_TO_VIDEO_DIT_MODELS = {
+    "YourTransformerModel": ("dits", "yourmodule", "YourTransformerClass"),
+}

-# Entry point for model registry
-EntryClass = YourTransformerModel
-```
-
-```python
-# In fastvideo/models/vaes/your_vae.py
-class YourVAEModel(...):
-    ...
-
-# Entry point for model registry
-EntryClass = YourVAEModel
-```
-
-Register pipeline config + sampling defaults in the unified registry:
-
-```python
-# In fastvideo/registry.py
-register_configs(
-    sampling_param_cls=YourSamplingParam,
-    pipeline_config_cls=YourPipelineConfig,
-    hf_model_paths=[
-        "org/your-model-id",
-    ],
-    model_detectors=[
-        lambda path: "your-model" in path.lower(),
-    ],
-)
+_VAE_MODELS = {
+    "YourVAEModel": ("vaes", "yourvae", "YourVAEClass"),
+}
 ```

 ## Step 2: Directory Structure
@@ -1,51 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from fastvideo import VideoGenerator
-from fastvideo.configs.sample import SamplingParam
-
-
-def main():
-    # Point this to your local diffusers model dir (or replace with a HF model ID).
-    model_path = "KyleShao/Cosmos-Predict2.5-2B-Diffusers"
-
-    generator = VideoGenerator.from_pretrained(
-        model_path,
-        num_gpus=1,
-        use_fsdp_inference=False,  # set True if GPU is out of memory
-        dit_cpu_offload=False,
-        vae_cpu_offload=False,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=True,
-    )
-
-    sampling_param = SamplingParam.from_pretrained(model_path)
-
-    # image2world example from official repo
-    image_path = "images/bus_terminal.jpg"
-
-    prompt = (
-        "A nighttime city bus terminal gradually shifts from stillness to subtle movement. "
-        "At first, multiple double-decker buses are parked under the glow of overhead lights, "
-        "with a central bus labeled '87D' facing forward and stationary. "
-        "As the video progresses, the bus in the middle moves ahead slowly, its headlights brightening the surrounding area "
-        "and casting reflections onto adjacent vehicles. "
-        "The motion creates space in the lineup, signaling activity within the otherwise quiet station. "
-        "It then comes to a smooth stop, resuming its position in line. "
-        "Overhead signage in Chinese characters remains illuminated, enhancing the vibrant, urban night scene."
-    )
-
-    generator.generate_video(
-        prompt,
-        sampling_param=sampling_param,
-        image_path=str(image_path),
-        num_cond_frames=1,
-        output_path="outputs_video/cosmos2_5_i2w.mp4",
-        save_video=True,
-    )
-
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
-
-
@@ -1,5 +1,4 @@
 from fastvideo import VideoGenerator
-from fastvideo.configs.sample import SamplingParam


 def main():
@@ -16,27 +15,19 @@ def main():
        pin_cpu_memory=True,
    )

-    # Load default sampling parameters (negative_prompt, resolution, steps, etc.)
-    sampling_param = SamplingParam.from_pretrained(model_path)
-
    prompt = (
-        "A high-definition video captures the precision of robotic welding in an industrial setting. "
-        "The first frame showcases a robotic arm, equipped with a welding torch, positioned over a large metal structure. "
-        "The welding process is in full swing, with bright sparks and intense light illuminating the scene, "
-        "creating a vivid display of blue and white hues. "
-        "A significant amount of smoke billows around the welding area, partially obscuring the view but emphasizing the heat and activity. "
-        "The background reveals parts of the workshop environment, including a ventilation system and various pieces of machinery, "
-        "indicating a busy and functional industrial workspace. "
-        "As the video progresses, the robotic arm maintains its steady position, continuing the welding process and moving to its left. "
-        "The welding torch consistently emits sparks and light, and the smoke continues to rise, diffusing slightly as it moves upward. "
-        "The metal surface beneath the torch shows ongoing signs of heating and melting. "
-        "The scene retains its industrial ambiance, with the welding sparks and smoke dominating the visual field, "
-        "underscoring the ongoing nature of the welding operation."
+       "A high-definition video captures the precision of robotic welding in an industrial setting. The first frame showcases a robotic arm, equipped with a welding torch, positioned over a large metal structure. The welding process is in full swing, with bright sparks and intense light illuminating the scene, creating a vivid display of blue and white hues. A significant amount of smoke billows around the welding area, partially obscuring the view but emphasizing the heat and activity. The background reveals parts of the workshop environment, including a ventilation system and various pieces of machinery, indicating a busy and functional industrial workspace. As the video progresses, the robotic arm maintains its steady position, continuing the welding process and moving to its left. The welding torch consistently emits sparks and light, and the smoke continues to rise, diffusing slightly as it moves upward. The metal surface beneath the torch shows ongoing signs of heating and melting. The scene retains its industrial ambiance, with the welding sparks and smoke dominating the visual field, underscoring the ongoing nature of the welding operation."
    )

-    generator.generate_video(
+    video = generator.generate_video(
        prompt,
-        sampling_param=sampling_param,
+        negative_prompt="",
+        height=704,
+        width=1280,
+        num_frames=77,
+        num_inference_steps=35,
+        guidance_scale=7.0,
+        fps=24,
        output_path="outputs_video/cosmos2_5_t2w.mp4",
        save_video=True,
    )
@@ -1,54 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from fastvideo import VideoGenerator
-from fastvideo.configs.sample import SamplingParam
-
-
-def main():
-    # Point this to your local diffusers model dir (or replace with a HF model ID).
-    model_path = "KyleShao/Cosmos-Predict2.5-2B-Diffusers"
-
-    generator = VideoGenerator.from_pretrained(
-        model_path,
-        num_gpus=1,
-        use_fsdp_inference=False,  # set True if GPU is out of memory
-        dit_cpu_offload=False,
-        vae_cpu_offload=False,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=True,
-    )
-
-    sampling_param = SamplingParam.from_pretrained(model_path)
-
-    # video2world example from official repo
-    video_path = "videos/robot_pouring.mp4"
-
-    prompt = (
-        "A robotic arm, primarily white with black joints and cables, is shown in a clean, modern indoor setting with a white tabletop. "
-        "The arm, equipped with a gripper holding a small, light green pitcher, is positioned above a clear glass containing a reddish-brown liquid and a spoon. "
-        "The robotic arm is in the process of pouring a transparent liquid into the glass. "
-        "To the left of the pitcher, there is an opened jar with a similar reddish-brown substance visible through its transparent body. "
-        "In the background, a vase with white flowers and a brown couch are partially visible, adding to the contemporary ambiance. "
-        "The lighting is bright, casting soft shadows on the table. "
-        "The robotic arm's movements are smooth and controlled, demonstrating precision in its task. "
-        "As the video progresses, the robotic arm completes the pour, leaving the glass half-filled with the reddish-brown liquid. "
-        "The jar remains untouched throughout the sequence, and the spoon inside the glass remains stationary. "
-        "The other robotic arm on the right side also stays stationary throughout the video. "
-        "The final frame captures the robotic arm with the pitcher finishing the pour, with the glass now filled to a higher level, while the pitcher is slightly tilted but still held securely by the gripper."
-    )
-
-    generator.generate_video(
-        prompt,
-        sampling_param=sampling_param,
-        video_path=str(video_path),
-        num_cond_frames=1,
-        output_path="outputs_video/cosmos2_5_v2w.mp4",
-        save_video=True,
-    )
-
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
-
-
@@ -39,4 +39,4 @@ def main():


 if __name__ == "__main__":
-    main()
+    main()
@@ -1,43 +0,0 @@
-from fastvideo import VideoGenerator
-import json
-# from fastvideo.configs.sample import SamplingParam
-
-OUTPUT_PATH = "video_samples_hy15_1080p"
-def main():
-    # FastVideo will automatically use the optimal default arguments for the
-    # model.
-    # If a local path is provided, FastVideo will make a best effort
-    # attempt to identify the optimal arguments.
-    generator = VideoGenerator.from_pretrained(
-        "weizhou03/HunyuanVideo-1.5-Diffusers-1080p-2SR", # 480p -> 720p -> 1080p
-        # or "weizhou03/HunyuanVideo-1.5-Diffusers-1080p" # 720p -> 1080p
-        # FastVideo will automatically handle distributed setup
-        num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
-        dit_cpu_offload=True,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
-        # image_encoder_cpu_offload=False,
-    )
-
-    prompt = (
-        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
-        "wide with interest. The playful yet serene atmosphere is complemented by soft "
-        "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
-    )
-
-    video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, negative_prompt="")
-
-    prompt2 = (
-        "A majestic lion strides across the golden savanna, its powerful frame "
-        "glistening under the warm afternoon sun. The tall grass ripples gently in "
-        "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
-        "embodying the raw energy of the wild. Low angle, steady tracking shot, "
-        "cinematic.")
-
-    video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True, negative_prompt="")
-
-
-if __name__ == "__main__":
-    main()
@@ -1,11 +1,165 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Basic example for HYWorld (HY-WorldPlay) video generation using FastVideo.
+
+This example replicates the same functionality as HY-WorldPlay/run.sh,
+demonstrating image-to-video generation with camera trajectory control.
+"""
+
+import time
+import math
+import numpy as np
+import imageio
+import torchvision
+from einops import rearrange
+
 from fastvideo import VideoGenerator
+from fastvideo.pipelines import ForwardBatch
+from fastvideo.utils import shallow_asdict, align_to
+from fastvideo.logger import init_logger
+
+from fastvideo.models.dits.hyworld.pose import pose_to_input, compute_latent_num
 from fastvideo.models.dits.hyworld.resolution_utils import get_resolution_from_image

+logger = init_logger(__name__)
+
+class HYWorldVideoGenerator(VideoGenerator):
+    """Extended VideoGenerator that adds HYWorld-specific parameters to batch.extra."""
+
+    def _generate_single_video(self, prompt: str, sampling_param=None, **kwargs):
+        """Override to add viewmats, Ks, and action to batch.extra."""
+        fastvideo_args = self.fastvideo_args
+        pipeline_config = fastvideo_args.pipeline_config
+
+        if sampling_param is None:
+            from fastvideo.configs.sample import SamplingParam
+            sampling_param = SamplingParam.from_pretrained(fastvideo_args.model_path)
+
+        # Update sampling param with kwargs
+        if kwargs:
+            for key, value in kwargs.items():
+                if hasattr(sampling_param, key):
+                    setattr(sampling_param, key, value)
+
+        # Get pose string from sampling_param or kwargs
+        pose = kwargs.get('pose', getattr(sampling_param, 'POSE', 'w-31'))
+        num_frames = kwargs.get('num_frames', getattr(sampling_param, 'num_frames', 125))
+
+        # Calculate number of latents
+        latent_num = compute_latent_num(num_frames)
+
+        # Convert pose to viewmats, Ks, and action
+        viewmats, Ks, action = pose_to_input(pose, latent_num)
+
+        # Convert to tensors and add batch dimension
+        viewmats = viewmats.unsqueeze(0)  # (1, T, 4, 4)
+        Ks = Ks.unsqueeze(0)  # (1, T, 3, 3)
+        action = action.unsqueeze(0)  # (1, T)
+
+        # Validate inputs
+        prompt = prompt.strip()
+        sampling_param = sampling_param.__class__(**shallow_asdict(sampling_param))
+        output_path = kwargs.get("output_path", sampling_param.output_path)
+        sampling_param.prompt = prompt
+
+        if sampling_param.negative_prompt is not None:
+            sampling_param.negative_prompt = sampling_param.negative_prompt.strip()
+
+        # Validate dimensions
+        if (sampling_param.height <= 0 or sampling_param.width <= 0 or
+            sampling_param.num_frames <= 0):
+            raise ValueError(
+                f"Height, width, and num_frames must be positive integers")
+
+        temporal_scale_factor = pipeline_config.vae_config.arch_config.temporal_compression_ratio
+        num_frames = sampling_param.num_frames
+        num_gpus = fastvideo_args.num_gpus
+        use_temporal_scaling_frames = pipeline_config.vae_config.use_temporal_scaling_frames
+
+        # Adjust number of frames based on number of GPUs
+        if use_temporal_scaling_frames:
+            orig_latent_num_frames = (num_frames - 1) // temporal_scale_factor + 1
+        else:
+            orig_latent_num_frames = sampling_param.num_frames // 17 * 3
+
+        if orig_latent_num_frames % fastvideo_args.num_gpus != 0:
+            if use_temporal_scaling_frames:
+                new_num_frames = (orig_latent_num_frames - 1) * temporal_scale_factor + 1
+            else:
+                divisor = math.lcm(3, num_gpus)
+                orig_latent_num_frames = (
+                    (orig_latent_num_frames + divisor - 1) // divisor) * divisor
+                new_num_frames = orig_latent_num_frames // 3 * 17
+
+            logger.info(
+                "Adjusting number of frames from %s to %s based on number of GPUs (%s)",
+                sampling_param.num_frames, new_num_frames, fastvideo_args.num_gpus)
+            sampling_param.num_frames = new_num_frames
+
+        # Calculate sizes
+        target_height = align_to(sampling_param.height, 16)
+        target_width = align_to(sampling_param.width, 16)
+
+        # Calculate latent sizes
+        latents_size = [(sampling_param.num_frames - 1) // 4 + 1,
+                        sampling_param.height // 8, sampling_param.width // 8]
+        n_tokens = latents_size[0] * latents_size[1] * latents_size[2]
+
+        # Prepare batch
+        batch = ForwardBatch(
+            **shallow_asdict(sampling_param),
+            eta=0.0,
+            n_tokens=n_tokens,
+            VSA_sparsity=fastvideo_args.VSA_sparsity,
+        )
+
+        # Add HYWorld-specific parameters to batch.extra
+        batch.extra['viewmats'] = viewmats
+        batch.extra['Ks'] = Ks
+        batch.extra['action'] = action
+        batch.extra['chunk_latent_frames'] = 16  # For bidirectional model
+
+        # Run inference
+        start_time = time.perf_counter()
+        output_batch = self.executor.execute_forward(batch, fastvideo_args)
+        samples = output_batch.output
+        logging_info = output_batch.logging_info
+
+        gen_time = time.perf_counter() - start_time
+        logger.info("Generated successfully in %.2f seconds", gen_time)
+
+        # Process outputs
+        videos = rearrange(samples, "b c t h w -> t b c h w")
+        frames = []
+        for x in videos:
+            x = torchvision.utils.make_grid(x, nrow=6)
+            x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
+            frames.append((x * 255).numpy().astype(np.uint8))
+
+        # Save video if requested
+        if batch.save_video:
+            imageio.mimsave(output_path, frames, fps=batch.fps, format="mp4")
+            logger.info("Saved video to %s", output_path)
+
+        if batch.return_frames:
+            return frames
+        else:
+            return {
+                "samples": samples,
+                "frames": frames,
+                "prompts": prompt,
+                "size": (target_height, target_width, batch.num_frames),
+                "generation_time": gen_time,
+                "logging_info": logging_info,
+                "trajectory": output_batch.trajectory_latents,
+                "trajectory_timesteps": output_batch.trajectory_timesteps,
+                "trajectory_decoded": output_batch.trajectory_decoded,
+            }
+
 # Default prompt from HY-WorldPlay run.sh
 DEFAULT_PROMPT = 'A paved pathway leads towards a stone arch bridge spanning a calm body of water.  Lush green trees and foliage line the path and the far bank of the water. A traditional-style pavilion with a tiered, reddish-brown roof sits on the far shore. The water reflects the surrounding greenery and the sky.  The scene is bathed in soft, natural light, creating a tranquil and serene atmosphere. The pathway is composed of large, rectangular stones, and the bridge is constructed of light gray stone.  The overall composition emphasizes the peaceful and harmonious nature of the landscape.'
 DEFAULT_IMAGE = 'https://raw.githubusercontent.com/Tencent-Hunyuan/HY-WorldPlay/main/assets/img/test.png'

-OUTPUT_PATH = "video_samples_hyworld"
 def main():
    import argparse

@@ -15,7 +169,7 @@ def main():
    parser.add_argument("--prompt", type=str, default=DEFAULT_PROMPT, help="Text prompt for video generation")
    parser.add_argument("--image", type=str, default=DEFAULT_IMAGE, help="Path or URL to input image")
    parser.add_argument("--pose", type=str, default='w-31', help="Pose string (e.g., 'a-31', 'w-31', 's-31', 'd-31')")
-    parser.add_argument("--output_path", type=str, default=OUTPUT_PATH, help="Output video path")
+    parser.add_argument("--output_path", type=str, default='video_samples_hyworld', help="Output video path")
    parser.add_argument("--num-frames", type=int, default=125, help="Number of frames")
    parser.add_argument("--seed", type=int, default=1, help="Random seed")
    parser.add_argument("--resolution", type=str, default="480p", help="Only support 480p for now")
@@ -31,7 +185,8 @@ def main():

    # Initialize generator
    print("\nInitializing VideoGenerator for HYWorld...")
-    generator = VideoGenerator.from_pretrained(
+    
+    generator = HYWorldVideoGenerator.from_pretrained(
        "FastVideo/HY-WorldPlay-Bidirectional-Diffusers",
        num_gpus=1,
        use_fsdp_inference=True,
@@ -43,12 +198,11 @@ def main():
    )

    # Generate video
-    # The pose string is automatically converted to camera matrices by the pipeline
    print("\nGenerating video...")
-    generator.generate_video(
+    start_time = time.time()
+    video = generator.generate_video(
        prompt=args.prompt,
        image_path=args.image,
-        pose=args.pose,  # Camera trajectory control
        output_path=args.output_path,
        save_video=True,
        negative_prompt="",
@@ -57,9 +211,13 @@ def main():
        height=HEIGHT,
        width=WIDTH,
        seed=args.seed,
+        pose=args.pose,
    )
+    elapsed = time.time() - start_time

-    print(f"\nVideo saved to: {args.output_path}")
+    print(f"\nVideo generated successfully!")
+    print(f"Saved to: {args.output_path}")
+    print(f"Time: {elapsed:.2f}s")


 if __name__ == "__main__":
@@ -4,7 +4,7 @@ export WANDB_BASE_URL="https://api.wandb.ai"
 export WANDB_MODE=online
 export TOKENIZERS_PARALLELISM=false

-MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
 DATA_DIR="data/crush-smol_processed_t2v_1_3b_ode_init/"
 VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
 NUM_GPUS=1
@@ -14,6 +14,7 @@ NUM_GPUS=1
 training_args=(
  --tracker_project_name "wan_ode_init"
  --output_dir "wan_ode_init_crush_smol"
+  --override_transformer_cls_name "CausalWanTransformer3DModel"
  --wandb_run_name "wan_ode_init_crush_smol"
  --max_train_steps 6000
  --train_batch_size 1
@@ -33,7 +34,7 @@ parallel_args=(
  --sp_size 1
  --tp_size 1
  --hsdp_replicate_dim 1
-  --hsdp_shard_dim $NUM_GPUS
+  --hsdp_shard_dim 1
 )

 # Model arguments
@@ -50,17 +51,20 @@ dataset_args=(

 # Validation arguments
 validation_args=(
-  --log-visualization
-  --visualization-steps 100
+  --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 50
+  --validation_sampling_steps "50"
+  --validation_guidance_scale "6.0"
 )

 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 1e-5
+  --learning_rate 6e-6
  --mixed_precision "bf16"
  --weight_only_checkpointing_steps 1000
  --training_state_checkpointing_steps 1000
-  --weight_decay 0.01
+  --weight_decay 1e-4
  --max_grad_norm 1.0
 )

@@ -5,7 +5,6 @@ from fastvideo.configs.models.vaes.base import VAEConfig
 from fastvideo.configs.models.audio import (LTX2AudioDecoderConfig,
                                            LTX2AudioEncoderConfig,
                                            LTX2VocoderConfig)
-from fastvideo.configs.models.upsamplers.base import UpsamplerConfig

 __all__ = [
    "ModelConfig",
@@ -15,5 +14,4 @@ __all__ = [
    "LTX2AudioEncoderConfig",
    "LTX2AudioDecoderConfig",
    "LTX2VocoderConfig",
-    "UpsamplerConfig",
 ]
@@ -7,9 +7,11 @@ from fastvideo.configs.models.dits.ltx2 import LTX2VideoConfig
 from fastvideo.configs.models.dits.stepvideo import StepVideoConfig
 from fastvideo.configs.models.dits.wanvideo import WanVideoConfig
 from fastvideo.configs.models.dits.hyworld import HYWorldConfig
+from fastvideo.configs.models.dits.wangamevideo import WanGameVideoConfig

 __all__ = [
    "HunyuanVideoConfig", "HunyuanVideo15Config", "WanVideoConfig",
    "StepVideoConfig", "CosmosVideoConfig", "Cosmos25VideoConfig",
-    "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig"
+    "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig",
+    "WanGameVideoConfig"
 ]
@@ -55,8 +55,6 @@ class HunyuanVideo15ArchConfig(DiTArchConfig):
            r"txt_in.refiner_blocks.\1.mlp.fc_out.\2",
            r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.norm_out\.linear\.(.*)$":
            r"txt_in.refiner_blocks.\1.adaLN_modulation.linear.\2",
-            r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.self_attn_qkv\.(.*)$":
-            r"txt_in.refiner_blocks.\1.self_attn_qkv.\2",

            # 2. txt_in_2 mapping:
            r"^context_embedder_2\.(.*)$":
@@ -8,8 +8,12 @@ def is_double_block(n: str, m) -> bool:
    return "double" in n and str.isdigit(n.split(".")[-1])


-def is_refiner_block(n: str, m) -> bool:
-    return "refiner" in n and str.isdigit(n.split(".")[-1])
+def is_single_block(n: str, m) -> bool:
+    return "single" in n and str.isdigit(n.split(".")[-1])
+
+
+# def is_refiner_block(n: str, m) -> bool:
+#     return "refiner" in n and str.isdigit(n.split(".")[-1])


 def is_txt_in(n: str, m) -> bool:
@@ -19,10 +23,10 @@ def is_txt_in(n: str, m) -> bool:
@dataclass
 class HYWorldArchConfig(DiTArchConfig):
    _fsdp_shard_conditions: list = field(
-        default_factory=lambda: [is_double_block, is_refiner_block])
+        default_factory=lambda: [is_double_block, is_single_block])

    _compile_conditions: list = field(
-        default_factory=lambda: [is_double_block, is_refiner_block, is_txt_in])
+        default_factory=lambda: [is_double_block, is_single_block, is_txt_in])

    param_names_mapping: dict = field(
        default_factory=lambda: {
@@ -50,7 +54,8 @@ class HYWorldArchConfig(DiTArchConfig):
            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.adaLN_modulation\.1\.(.*)$":
            r"txt_in.refiner_blocks.\1.adaLN_modulation.linear.\2",

-            # 2. time_in mappings:
+            # 2. time_in mappings (HYWorld uses TimestepEmbedder directly,
+            #    but FastVideo model inherits HunyuanVideo15TimeEmbedding with timestep_embedder):
            r"^time_in\.mlp\.0\.(.*)$":
            r"time_in.timestep_embedder.mlp.fc_in.\1",
            r"^time_in\.mlp\.2\.(.*)$":
@@ -1,6 +1,4 @@
 from dataclasses import dataclass, field
-
-import torch
 from fastvideo.configs.models.dits.wanvideo import WanVideoArchConfig, WanVideoConfig


@@ -78,14 +76,8 @@ class MatrixGameWanVideoArchConfig(WanVideoArchConfig):
    image_dim: int = 1280


-def _is_transformer_block(param_name: str, module: torch.nn.Module) -> bool:
-    return bool("blocks" in param_name and param_name.split(".")[-1].isdigit())
-
-
@dataclass
 class MatrixGameWanVideoConfig(WanVideoConfig):
    arch_config: MatrixGameWanVideoArchConfig = field(
        default_factory=MatrixGameWanVideoArchConfig)
    prefix: str = "Wan"
-    _compile_conditions: list = field(
-        default_factory=lambda: [_is_transformer_block])
@@ -0,0 +1,122 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
+
+
+def is_blocks(n: str, m) -> bool:
+    return "blocks" in n and str.isdigit(n.split(".")[-1])
+
+
+@dataclass
+class WanGameVideoArchConfig(DiTArchConfig):
+    _fsdp_shard_conditions: list = field(default_factory=lambda: [is_blocks])
+
+    param_names_mapping: dict = field(
+        default_factory=lambda: {
+            r"^patch_embedding\.(.*)$":
+            r"patch_embedding.proj.\1",
+            r"^condition_embedder\.text_embedder\.linear_1\.(.*)$":
+            r"condition_embedder.text_embedder.fc_in.\1",
+            r"^condition_embedder\.text_embedder\.linear_2\.(.*)$":
+            r"condition_embedder.text_embedder.fc_out.\1",
+            r"^condition_embedder\.time_embedder\.linear_1\.(.*)$":
+            r"condition_embedder.time_embedder.mlp.fc_in.\1",
+            r"^condition_embedder\.time_embedder\.linear_2\.(.*)$":
+            r"condition_embedder.time_embedder.mlp.fc_out.\1",
+            r"^condition_embedder\.time_proj\.(.*)$":
+            r"condition_embedder.time_modulation.linear.\1",
+            r"^condition_embedder\.image_embedder\.ff\.net\.0\.proj\.(.*)$":
+            r"condition_embedder.image_embedder.ff.fc_in.\1",
+            r"^condition_embedder\.image_embedder\.ff\.net\.2\.(.*)$":
+            r"condition_embedder.image_embedder.ff.fc_out.\1",
+            r"^blocks\.(\d+)\.attn1\.to_q\.(.*)$":
+            r"blocks.\1.to_q.\2",
+            r"^blocks\.(\d+)\.attn1\.to_k\.(.*)$":
+            r"blocks.\1.to_k.\2",
+            r"^blocks\.(\d+)\.attn1\.to_v\.(.*)$":
+            r"blocks.\1.to_v.\2",
+            r"^blocks\.(\d+)\.attn1\.to_out\.0\.(.*)$":
+            r"blocks.\1.to_out.\2",
+            r"^blocks\.(\d+)\.attn1\.norm_q\.(.*)$":
+            r"blocks.\1.norm_q.\2",
+            r"^blocks\.(\d+)\.attn1\.norm_k\.(.*)$":
+            r"blocks.\1.norm_k.\2",
+            r"^blocks\.(\d+)\.attn2\.to_out\.0\.(.*)$":
+            r"blocks.\1.attn2.to_out.\2",
+            r"^blocks\.(\d+)\.ffn\.net\.0\.proj\.(.*)$":
+            r"blocks.\1.ffn.fc_in.\2",
+            r"^blocks\.(\d+)\.ffn\.net\.2\.(.*)$":
+            r"blocks.\1.ffn.fc_out.\2",
+            r"^blocks\.(\d+)\.norm2\.(.*)$":
+            r"blocks.\1.self_attn_residual_norm.norm.\2",
+        })
+
+    # Reverse mapping for saving checkpoints: custom -> hf
+    reverse_param_names_mapping: dict = field(default_factory=lambda: {})
+
+    # Some LoRA adapters use the original official layer names instead of hf layer names,
+    # so apply this before the param_names_mapping
+    lora_param_names_mapping: dict = field(
+        default_factory=lambda: {
+            r"^blocks\.(\d+)\.self_attn\.q\.(.*)$": r"blocks.\1.attn1.to_q.\2",
+            r"^blocks\.(\d+)\.self_attn\.k\.(.*)$": r"blocks.\1.attn1.to_k.\2",
+            r"^blocks\.(\d+)\.self_attn\.v\.(.*)$": r"blocks.\1.attn1.to_v.\2",
+            r"^blocks\.(\d+)\.self_attn\.o\.(.*)$":
+            r"blocks.\1.attn1.to_out.0.\2",
+            r"^blocks\.(\d+)\.cross_attn\.q\.(.*)$": r"blocks.\1.attn2.to_q.\2",
+            r"^blocks\.(\d+)\.cross_attn\.k\.(.*)$": r"blocks.\1.attn2.to_k.\2",
+            r"^blocks\.(\d+)\.cross_attn\.v\.(.*)$": r"blocks.\1.attn2.to_v.\2",
+            r"^blocks\.(\d+)\.cross_attn\.o\.(.*)$":
+            r"blocks.\1.attn2.to_out.0.\2",
+            r"^blocks\.(\d+)\.ffn\.0\.(.*)$": r"blocks.\1.ffn.fc_in.\2",
+            r"^blocks\.(\d+)\.ffn\.2\.(.*)$": r"blocks.\1.ffn.fc_out.\2",
+        })
+
+    patch_size: tuple[int, int, int] = (1, 2, 2)
+    text_len = 512
+    num_attention_heads: int = 40
+    attention_head_dim: int = 128
+    in_channels: int = 16
+    out_channels: int = 16
+    text_dim: int = 4096
+    freq_dim: int = 256
+    ffn_dim: int = 13824
+    num_layers: int = 40
+    cross_attn_norm: bool = True
+    qk_norm: str = "rms_norm_across_heads"
+    eps: float = 1e-6
+    image_dim: int | None = None
+    added_kv_proj_dim: int | None = None
+    rope_max_seq_len: int = 1024
+    pos_embed_seq_len: int | None = None
+    exclude_lora_layers: list[str] = field(default_factory=lambda: ["embedder"])
+
+    # Wan MoE
+    boundary_ratio: float | None = None
+
+    # Causal Wan
+    local_attn_size: int = 6  # Window size for temporal local attention (-1 indicates global attention)
+    sink_size: int = 0  # Size of the attention sink, we keep the first `sink_size` frames unchanged when rolling the KV cache
+    num_frames_per_block: int = 3
+    sliding_window_num_frames: int = 21
+
+    def __post_init__(self):
+        super().__post_init__()
+        self.out_channels = self.out_channels or self.in_channels
+        self.hidden_size = self.num_attention_heads * self.attention_head_dim
+        self.num_channels_latents = self.out_channels
+
+
+@dataclass
+class WanGameVideoConfig(DiTConfig):
+    arch_config: DiTArchConfig = field(default_factory=WanGameVideoArchConfig)
+
+    prefix: str = "WanGame"
+
+
+@dataclass
+class WanLingBotVideoConfig(DiTConfig):
+    arch_config: DiTArchConfig = field(default_factory=WanGameVideoArchConfig)
+
+    prefix: str = "WanLingBot"
@@ -87,10 +87,10 @@ class CLIPVisionConfig(ImageEncoderConfig):
    arch_config: ImageEncoderArchConfig = field(
        default_factory=CLIPVisionArchConfig)

-    num_hidden_layers_override: int | None = 31
+    num_hidden_layers_override: int | None = None
    require_post_norm: bool | None = None
-    enable_scale: bool = False
-    is_causal: bool = False
+    enable_scale: bool = True
+    is_causal: bool = True
    prefix: str = "clip"


@@ -1,6 +0,0 @@
-from fastvideo.configs.models.upsamplers.hunyuan15 import SRTo720pUpsamplerConfig, SRTo1080pUpsamplerConfig
-from fastvideo.configs.models.upsamplers.base import UpsamplerConfig
-
-__all__ = [
-    "SRTo720pUpsamplerConfig", "SRTo1080pUpsamplerConfig", "UpsamplerConfig"
-]
@@ -1,7 +0,0 @@
-from dataclasses import dataclass
-from fastvideo.configs.models.base import ModelConfig
-
-
-@dataclass
-class UpsamplerConfig(ModelConfig):
-    pass
@@ -1,20 +0,0 @@
-from dataclasses import dataclass
-from fastvideo.configs.models.upsamplers.base import UpsamplerConfig
-
-
-@dataclass
-class SRTo720pUpsamplerConfig(UpsamplerConfig):
-    in_channels: int = 0
-    out_channels: int = 0
-    hidden_channels: int = 64
-    num_blocks: int = 6
-    global_residual: bool = False
-
-
-@dataclass
-class SRTo1080pUpsamplerConfig(UpsamplerConfig):
-    z_channels: int = 0
-    out_channels: int = 0
-    block_out_channels: tuple[int, ...] = (0, 0)
-    num_res_blocks: int = 2
-    is_residual: bool = False
@@ -6,7 +6,8 @@ from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
 from fastvideo.configs.pipelines.hyworld import HYWorldConfig
 from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
-from fastvideo.registry import get_pipeline_config_cls_from_name
+from fastvideo.configs.pipelines.registry import (
+    get_pipeline_config_cls_from_name)
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
 from fastvideo.configs.pipelines.wan import (SelfForcingWanT2V480PConfig,
                                             WanI2V480PConfig, WanI2V720PConfig,
@@ -8,7 +8,7 @@ from typing import Any, cast
 import torch

 from fastvideo.configs.models import (DiTConfig, EncoderConfig, ModelConfig,
-                                      VAEConfig, UpsamplerConfig)
+                                      VAEConfig)
 from fastvideo.configs.models.encoders import BaseEncoderOutput
 from fastvideo.configs.utils import update_config_from_args
 from fastvideo.logger import init_logger
@@ -44,15 +44,12 @@ class PipelineConfig:
    # Video generation parameters
    embedded_cfg_scale: float = 6.0
    flow_shift: float | None = None
-    flow_shift_sr: float | None = None
    disable_autocast: bool = False
    is_causal: bool = False

    # Model configuration
    dit_config: DiTConfig = field(default_factory=DiTConfig)
    dit_precision: str = "bf16"
-    upsampler_config: UpsamplerConfig = field(default_factory=UpsamplerConfig)
-    upsampler_precision: str = "fp32"

    # VAE configuration
    vae_config: VAEConfig = field(default_factory=VAEConfig)
@@ -219,24 +216,6 @@ class PipelineConfig:
            "Comma-separated list of denoising steps (e.g., '1000,757,522')",
        )

-        # STA (Sliding Tile Attention) parameters
-        parser.add_argument(
-            f"--{prefix_with_dot}STA-mode",
-            type=str,
-            dest=f"{prefix_with_dot.replace('-', '_')}STA_mode",
-            default=PipelineConfig.STA_mode.value,
-            choices=[mode.value for mode in STA_Mode],
-            help=
-            "STA mode: STA_inference, STA_searching, STA_tuning, STA_tuning_cfg, None",
-        )
-        parser.add_argument(
-            f"--{prefix_with_dot}skip-time-steps",
-            type=int,
-            dest=f"{prefix_with_dot.replace('-', '_')}skip_time_steps",
-            default=PipelineConfig.skip_time_steps,
-            help="Number of time steps to warmup (full attention) for STA",
-        )
-
        # Add VAE configuration arguments
        from fastvideo.configs.models.vaes.base import VAEConfig
        VAEConfig.add_cli_args(parser, prefix=f"{prefix_with_dot}vae-config")
@@ -266,7 +245,8 @@ class PipelineConfig:
        """
        use the pipeline class setting from model_path to match the pipeline config
        """
-        from fastvideo.registry import get_pipeline_config_cls_from_name
+        from fastvideo.configs.pipelines.registry import (
+            get_pipeline_config_cls_from_name)
        pipeline_config_cls = get_pipeline_config_cls_from_name(model_path)

        return cast(PipelineConfig, pipeline_config_cls(model_path=model_path))
@@ -280,7 +260,8 @@ class PipelineConfig:
        kwargs: dictionary of kwargs
        config_cli_prefix: prefix of CLI arguments for this PipelineConfig instance
        """
-        from fastvideo.registry import get_pipeline_config_cls_from_name
+        from fastvideo.configs.pipelines.registry import (
+            get_pipeline_config_cls_from_name)

        prefix_with_dot = f"{config_cli_prefix}." if (config_cli_prefix.strip()
                                                      != "") else ""
@@ -317,12 +298,6 @@ class PipelineConfig:
        # 4. Update PipelineConfig from CLI arguments if provided
        kwargs[prefix_with_dot + 'model_path'] = model_path
        pipeline_config.update_config_from_dict(kwargs, config_cli_prefix)
-
-        # Convert STA_mode string to enum if necessary
-        if isinstance(pipeline_config.STA_mode, str) and not isinstance(
-                pipeline_config.STA_mode, STA_Mode):
-            pipeline_config.STA_mode = STA_Mode(pipeline_config.STA_mode)
-
        return pipeline_config

    def check_pipeline_config(self) -> None:
@@ -11,8 +11,7 @@ from fastvideo.configs.models.dits import HunyuanVideo15Config
 from fastvideo.configs.models.encoders import (BaseEncoderOutput,
                                               Qwen2_5_VLConfig, T5Config)
 from fastvideo.configs.models.vaes import Hunyuan15VAEConfig
-from fastvideo.configs.models.upsamplers import SRTo720pUpsamplerConfig, SRTo1080pUpsamplerConfig
-from fastvideo.configs.pipelines.base import PipelineConfig, UpsamplerConfig
+from fastvideo.configs.pipelines.base import PipelineConfig

 PROMPT_TEMPLATE_TOKEN_LENGTH = 108

@@ -132,35 +131,9 @@ class Hunyuan15T2V480PConfig(PipelineConfig):
        self.vae_config.load_decoder = True


-@dataclass
-class Hunyuan15I2V480PStepDistilledConfig(Hunyuan15T2V480PConfig):
-    flow_shift: int = 7
-
-
@dataclass
 class Hunyuan15T2V720PConfig(Hunyuan15T2V480PConfig):
    """Base configuration for HunYuan pipeline architecture."""

    # HunyuanConfig-specific parameters with defaults
    flow_shift: int = 9
-
-
-@dataclass
-class Hunyuan15I2V720PConfig(Hunyuan15T2V720PConfig):
-    """Base configuration for HunYuan pipeline architecture."""
-
-    # HunyuanConfig-specific parameters with defaults
-    flow_shift: int = 7
-
-
-@dataclass
-class Hunyuan15SR1080PConfig(Hunyuan15T2V720PConfig):
-    """Base configuration for HunYuan pipeline architecture."""
-
-    # HunyuanConfig-specific parameters with defaults
-    flow_shift: int = 7
-    flow_shift_sr: int = 2
-    upsampler_config: tuple[UpsamplerConfig, ...] = field(
-        default_factory=lambda:
-        (SRTo720pUpsamplerConfig(), SRTo1080pUpsamplerConfig()))
-    upsampler_precision: str = "fp32"
@@ -0,0 +1,218 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Registry for pipeline weight-specific configurations."""
+
+import os
+from collections.abc import Callable
+
+from fastvideo.configs.pipelines.base import PipelineConfig
+from fastvideo.configs.pipelines.cosmos import CosmosConfig
+from fastvideo.configs.pipelines.cosmos2_5 import Cosmos25Config
+from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
+from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
+from fastvideo.configs.pipelines.hyworld import HYWorldConfig
+from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
+from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
+from fastvideo.configs.pipelines.longcat import LongCatT2V480PConfig
+from fastvideo.configs.pipelines.turbodiffusion import (
+    TurboDiffusionT2V_1_3B_Config, TurboDiffusionT2V_14B_Config,
+    TurboDiffusionI2V_A14B_Config)
+
+# isort: off
+from fastvideo.configs.pipelines.wan import (
+    FastWan2_1_T2V_480P_Config, FastWan2_2_TI2V_5B_Config,
+    Wan2_2_I2V_A14B_Config, Wan2_2_T2V_A14B_Config, Wan2_2_TI2V_5B_Config,
+    WanI2V480PConfig, WanI2V720PConfig, WanT2V480PConfig, WanT2V720PConfig,
+    SelfForcingWanT2V480PConfig, WANV2VConfig, SelfForcingWan2_2_T2V480PConfig,
+    MatrixGameI2V480PConfig, WanGameI2V480PConfig)
+# isort: on
+from fastvideo.logger import init_logger
+from fastvideo.utils import (maybe_download_model_index,
+                             verify_model_config_and_directory)
+
+logger = init_logger(__name__)
+
+# Registry maps specific model weights to their config classes
+PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
+    "FastVideo/FastHunyuan-diffusers": FastHunyuanConfig,
+    "hunyuanvideo-community/HunyuanVideo": HunyuanConfig,
+    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v":
+    Hunyuan15T2V480PConfig,
+    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v":
+    Hunyuan15T2V720PConfig,
+    "FastVideo/HY-WorldPlay-Bidirectional-Diffusers": HYWorldConfig,
+    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V480PConfig,
+    "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers": WanI2V480PConfig,
+    "weizhou03/Wan2.1-Game-Fun-1.3B-InP-Diffusers": WanGameI2V480PConfig,
+    "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers": WANV2VConfig,
+    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V480PConfig,
+    "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V720PConfig,
+    "Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V720PConfig,
+    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWan2_1_T2V_480P_Config,
+    "FastVideo/FastWan2.1-T2V-14B-480P-Diffusers": FastWan2_1_T2V_480P_Config,
+    "FastVideo/FastWan2.2-TI2V-5B-Diffusers": FastWan2_2_TI2V_5B_Config,
+    "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VConfig,
+    "FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers": WanT2V720PConfig,
+    "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers": SelfForcingWanT2V480PConfig,
+    "rand0nmr/SFWan2.2-T2V-A14B-Diffusers": SelfForcingWan2_2_T2V480PConfig,
+    "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers":
+    SelfForcingWan2_2_T2V480PConfig,
+    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_Config,
+    "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_Config,
+    "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_Config,
+    "nvidia/Cosmos-Predict2-2B-Video2World": CosmosConfig,
+    "KyleShao/Cosmos-Predict2.5-2B-Diffusers": Cosmos25Config,
+    "FastVideo/Matrix-Game-2.0-Base-Diffusers": MatrixGameI2V480PConfig,
+    "FastVideo/Matrix-Game-2.0-GTA-Diffusers": MatrixGameI2V480PConfig,
+    "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers": MatrixGameI2V480PConfig,
+    # LongCat Video models
+    "FastVideo/LongCat-Video-T2V-Diffusers": LongCatT2V480PConfig,
+    "FastVideo/LongCat-Video-I2V-Diffusers": LongCatT2V480PConfig,
+    "FastVideo/LongCat-Video-VC-Diffusers": LongCatT2V480PConfig,
+    # LTX-2 models
+    "Lightricks/LTX-2": LTX2T2VConfig,
+    "converted/ltx2_diffusers": LTX2T2VConfig,
+    # TurboDiffusion models
+    "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers": TurboDiffusionT2V_1_3B_Config,
+    "loayrashid/TurboWan2.1-T2V-14B-Diffusers": TurboDiffusionT2V_14B_Config,
+    "loayrashid/TurboWan2.2-I2V-A14B-Diffusers": TurboDiffusionI2V_A14B_Config,
+    # Add other specific weight variants
+}
+
+# For determining pipeline type from model ID
+PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
+    "longcatimagetovideo":
+    lambda id: "longcatimagetovideo" in id.lower(),
+    "longcatvideocontinuation":
+    lambda id: "longcatvideocontinuation" in id.lower(),
+    "longcat":
+    lambda id: "longcat" in id.lower(),
+    "hunyuan":
+    lambda id: "hunyuan" in id.lower(),
+    "hunyuan15":
+    lambda id: "hunyuan15" in id.lower(),
+    "hyworld":
+    lambda id: "hyworld" in id.lower(),
+    "matrixgame":
+    lambda id: "matrix-game" in id.lower() or "matrixgame" in id.lower(),
+    "wangameactionimagetovideo":
+    lambda id: "wangameactionimagetovideo" in id.lower(),
+    "wanpipeline":
+    lambda id: "wanpipeline" in id.lower(),
+    "wanimagetovideo":
+    lambda id: "wanimagetovideo" in id.lower(),
+    "wandmdpipeline":
+    lambda id: "wandmdpipeline" in id.lower(),
+    "wancausaldmdpipeline":
+    lambda id: "wancausaldmdpipeline" in id.lower(),
+    "stepvideo":
+    lambda id: "stepvideo" in id.lower(),
+    "cosmos":
+    lambda id: "cosmos" in id.lower() and ("2.5" not in id.lower(
+    ) and "2_5" not in id.lower() and "25" not in id.lower()),
+    "cosmos25":
+    lambda id: "cosmos25" in id.lower(),
+    "turbodiffusion":
+    lambda id: "turbodiffusion" in id.lower() or "turbowan" in id.lower(),
+    "ltx2":
+    lambda id: "ltx2" in id.lower() or "ltx-2" in id.lower(),
+    # Add other pipeline architecture detectors
+}
+
+# Fallback configs when exact match isn't found but architecture is detected
+PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
+    "longcatimagetovideo": LongCatT2V480PConfig,
+    "longcatvideocontinuation": LongCatT2V480PConfig,
+    "longcat": LongCatT2V480PConfig,
+    "cosmos25": Cosmos25Config,
+    "hunyuan":
+    HunyuanConfig,  # Base Hunyuan config as fallback for any Hunyuan variant
+    "matrixgame": MatrixGameI2V480PConfig,
+    "wangameactionimagetovideo": WanGameI2V480PConfig,
+    "hunyuan15":
+    Hunyuan15T2V480PConfig,  # Base Hunyuan15 config as fallback for any Hunyuan15 variant
+    "hyworld":
+    HYWorldConfig,  # HYWorld-specific config as fallback for any HYWorld variant
+    "wanpipeline":
+    WanT2V480PConfig,  # Base Wan config as fallback for any Wan variant
+    "wanimagetovideo": WanI2V480PConfig,
+    "wandmdpipeline": FastWan2_1_T2V_480P_Config,
+    "wancausaldmdpipeline": SelfForcingWanT2V480PConfig,
+    "stepvideo": StepVideoT2VConfig,
+    "turbodiffusion": TurboDiffusionT2V_1_3B_Config,
+    "ltx2": LTX2T2VConfig,
+    # Other fallbacks by architecture
+}
+
+
+def get_pipeline_config_cls_from_name(
+        pipeline_name_or_path: str) -> type[PipelineConfig]:
+    """Get the appropriate configuration class for a given pipeline name or path.
+
+    This function implements a multi-step lookup process to find the most suitable
+    configuration class for a given pipeline. It follows this order:
+    1. Exact match in the PIPE_NAME_TO_CONFIG
+    2. Partial match in the PIPE_NAME_TO_CONFIG
+    3. Fallback to class name in the model_index.json
+    4. else raise an error
+
+    Args:
+        pipeline_name_or_path (str): The name or path of the pipeline. This can be:
+            - A registered model ID (e.g., "FastVideo/FastHunyuan-diffusers")
+            - A local path to a model directory
+            - A model ID that will be downloaded
+
+    Returns:
+        Type[PipelineConfig]: The configuration class that best matches the pipeline.
+            This will be one of:
+            - A specific weight configuration class if an exact match is found
+            - A fallback configuration class based on the pipeline architecture
+            - The base PipelineConfig class if no matches are found
+
+    Note:
+        - For local paths, the function will verify the model configuration
+        - For remote models, it will attempt to download the model index
+        - Warning messages are logged when falling back to less specific configurations
+    """
+
+    pipeline_config_cls: type[PipelineConfig] | None = None
+
+    # First try exact match for specific weights
+    if pipeline_name_or_path in PIPE_NAME_TO_CONFIG:
+        pipeline_config_cls = PIPE_NAME_TO_CONFIG[pipeline_name_or_path]
+        return pipeline_config_cls
+
+    # Try partial matches (for local paths that might include the weight ID)
+    for registered_id, config_class in PIPE_NAME_TO_CONFIG.items():
+        if registered_id in pipeline_name_or_path:
+            pipeline_config_cls = config_class
+            break
+
+    # If no match, try to use the fallback config
+    if pipeline_config_cls is None:
+        if os.path.exists(pipeline_name_or_path):
+            config = verify_model_config_and_directory(pipeline_name_or_path)
+        else:
+            config = maybe_download_model_index(pipeline_name_or_path)
+        logger.warning(
+            "Trying to use the config from the model_index.json. FastVideo may not correctly identify the optimal config for this model in this situation."
+        )
+
+        pipeline_name = config["_class_name"]
+        # Try to determine pipeline architecture for fallback
+        for pipeline_type, detector in PIPELINE_DETECTOR.items():
+            if detector(pipeline_name.lower()):
+                pipeline_config_cls = PIPELINE_FALLBACK_CONFIG.get(
+                    pipeline_type)
+                break
+
+        if pipeline_config_cls is not None:
+            logger.warning(
+                "No match found for pipeline %s, using fallback config %s.",
+                pipeline_name_or_path, pipeline_config_cls)
+
+    if pipeline_config_cls is None:
+        raise ValueError(
+            f"No match found for pipeline {pipeline_name_or_path}, please check the pipeline name or path."
+        )
+
+    return pipeline_config_cls
@@ -7,6 +7,7 @@ import torch
 from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
 from fastvideo.configs.models.dits import WanVideoConfig
 from fastvideo.configs.models.dits.matrixgame import MatrixGameWanVideoConfig
+from fastvideo.configs.models.dits.wangamevideo import WanGameVideoConfig
 from fastvideo.configs.models.encoders import (BaseEncoderOutput,
                                               CLIPVisionConfig, T5Config,
                                               WAN2_1ControlCLIPVisionConfig)
@@ -217,3 +218,23 @@ class MatrixGameI2V480PConfig(WanI2V480PConfig):
    context_noise: int = 0
    num_frames_per_block: int = 3
    # sliding_window_num_frames: int = 15
+
+
+# =============================================
+# ============= WanGame ======================
+# =============================================
+@dataclass
+class WanGameI2V480PConfig(WanI2V480PConfig):
+    """Configuration for WanGame image-to-video pipeline."""
+    dit_config: DiTConfig = field(default_factory=WanGameVideoConfig)
+
+    image_encoder_config: EncoderConfig = field(
+        default_factory=WAN2_1ControlCLIPVisionConfig)
+
+    is_causal: bool = True
+    flow_shift: float | None = 5.0
+    dmd_denoising_steps: list[int] | None = field(
+        default_factory=lambda: [1000, 666, 333])
+    warp_denoising_step: bool = True
+    context_noise: int = 0
+    num_frames_per_block: int = 3
@@ -28,9 +28,6 @@ class SamplingParam:
    keyboard_cond: Any | None = None  # Shape: (B, T, K)
    grid_sizes: Any | None = None  # Shape: (3,) [F,H,W]

-    # Camera control inputs (HYWorld)
-    pose: str | None = None  # Camera trajectory: pose string (e.g., 'w-31') or JSON file path
-
    # Refine inputs (LongCat 480p->720p upscaling)
    # Path-based refine (load stage1 video from disk, e.g. MP4)
    refine_from: str | None = None  # Path to stage1 video (480p output from distill)
@@ -58,13 +55,10 @@ class SamplingParam:
    num_frames_round_down: bool = False  # Whether to round down num_frames if it's not divisible by num_gpus
    height: int = 720
    width: int = 1280
-    height_sr: int = 1072
-    width_sr: int = 1920
    fps: int = 24

    # Denoising parameters
    num_inference_steps: int = 50
-    num_inference_steps_sr: int = 50
    guidance_scale: float = 1.0
    guidance_rescale: float = 0.0
    boundary_ratio: float | None = None
@@ -98,7 +92,8 @@ class SamplingParam:

    @classmethod
    def from_pretrained(cls, model_path: str) -> "SamplingParam":
-        from fastvideo.registry import get_sampling_param_cls_for_name
+        from fastvideo.configs.sample.registry import (
+            get_sampling_param_cls_for_name)
        sampling_cls = get_sampling_param_cls_for_name(model_path)
        if sampling_cls is not None:
            sampling_param: SamplingParam = sampling_cls()
@@ -5,19 +5,15 @@ from fastvideo.configs.sample.base import SamplingParam


@dataclass
-class Cosmos25SamplingParamBase(SamplingParam):
-    height: int = 704
-    width: int = 1280
-    num_frames: int = 77
+class Cosmos_Predict2_5_2B_Diffusers_SamplingParam(SamplingParam):
+    """Defaults for Cosmos 2.5 (Predict2.5) text-to-video diffusers-format model."""
+
+    height: int = 480
+    width: int = 832
+    num_frames: int = 121
    fps: int = 24
-    seed: int = 0

    guidance_scale: float = 7.0
-    negative_prompt: str = (
-        "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, "
-        "over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, "
-        "underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky movements, "
-        "low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, fake elements, "
-        "unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. "
-        "Overall, the video is of poor quality.")
+    # Official Cosmos2.5 sampling uses empty string as unconditional.
+    negative_prompt: str = ""
    num_inference_steps: int = 35
@@ -28,33 +28,7 @@ class Hunyuan15_480P_SamplingParam(SamplingParam):
            np.linspace(1.0, 0.0, self.num_inference_steps + 1)[:-1])


-@dataclass
-class Hunyuan15_480P_StepDistilled_I2V_SamplingParam(
-        Hunyuan15_480P_SamplingParam):
-    num_inference_steps: int = 12
-
-    height: int = 720
-    width: int = 1280
-    guidance_scale: float = 1.0
-
-
@dataclass
 class Hunyuan15_720P_SamplingParam(Hunyuan15_480P_SamplingParam):
    height: int = 720
    width: int = 1280
-
-
-@dataclass
-class Hunyuan15_720P_Distilled_I2V_SamplingParam(Hunyuan15_720P_SamplingParam):
-    guidance_scale: float = 1.0
-
-
-@dataclass
-class Hunyuan15_SR_1080P_SamplingParam(Hunyuan15_480P_SamplingParam):
-    height_sr: int = 1072
-    width_sr: int = 1920
-
-    num_inference_steps: int = 12
-    num_inference_steps_sr: int = 8
-
-    guidance_scale: float = 1.0
@@ -0,0 +1,210 @@
+# SPDX-License-Identifier: Apache-2.0
+import os
+from collections.abc import Callable
+from typing import Any
+
+from fastvideo.configs.sample.hunyuan import (FastHunyuanSamplingParam,
+                                              HunyuanSamplingParam)
+from fastvideo.configs.sample.hunyuan15 import Hunyuan15_480P_SamplingParam, Hunyuan15_720P_SamplingParam
+from fastvideo.configs.sample.hyworld import HYWorld_SamplingParam
+from fastvideo.configs.sample.stepvideo import StepVideoT2VSamplingParam
+
+from fastvideo.configs.sample.cosmos import Cosmos_Predict2_2B_Video2World_SamplingParam
+from fastvideo.configs.sample.cosmos2_5 import Cosmos_Predict2_5_2B_Diffusers_SamplingParam
+from fastvideo.configs.sample.ltx2 import LTX2SamplingParam
+
+# isort: off
+from fastvideo.configs.sample.wan import (
+    FastWanT2V480P_SamplingParam,
+    Wan2_1_Fun_1_3B_InP_SamplingParam,
+    Wan2_2_I2V_A14B_SamplingParam,
+    Wan2_2_T2V_A14B_SamplingParam,
+    Wan2_2_TI2V_5B_SamplingParam,
+    WanI2V_14B_480P_SamplingParam,
+    WanI2V_14B_720P_SamplingParam,
+    WanT2V_1_3B_SamplingParam,
+    WanT2V_14B_SamplingParam,
+    Wan2_1_Fun_1_3B_Control_SamplingParam,
+    SelfForcingWan2_1_T2V_1_3B_480P_SamplingParam,
+    SelfForcingWan2_2_T2V_A14B_480P_SamplingParam,
+    MatrixGame2_SamplingParam,
+)
+from fastvideo.configs.sample.turbodiffusion import (
+    TurboDiffusionT2V_1_3B_SamplingParam,
+    TurboDiffusionT2V_14B_SamplingParam,
+    TurboDiffusionI2V_A14B_SamplingParam,
+)
+# isort: on
+from fastvideo.logger import init_logger
+from fastvideo.utils import (maybe_download_model_index,
+                             verify_model_config_and_directory)
+
+logger = init_logger(__name__)
+# Registry maps specific model weights to their config classes
+SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
+    "FastVideo/FastHunyuan-diffusers": FastHunyuanSamplingParam,
+    "hunyuanvideo-community/HunyuanVideo": HunyuanSamplingParam,
+    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v":
+    Hunyuan15_480P_SamplingParam,
+    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v":
+    Hunyuan15_720P_SamplingParam,
+    "FastVideo/HY-WorldPlay-Bidirectional-Diffusers": HYWorld_SamplingParam,
+    "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VSamplingParam,
+
+    # Wan2.1
+    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V_1_3B_SamplingParam,
+    "Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V_14B_SamplingParam,
+    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V_14B_480P_SamplingParam,
+    "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V_14B_720P_SamplingParam,
+    "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers":
+    Wan2_1_Fun_1_3B_InP_SamplingParam,
+    "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers":
+    Wan2_1_Fun_1_3B_Control_SamplingParam,
+
+    # Wan2.2
+    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
+    "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers":
+    Wan2_2_TI2V_5B_SamplingParam,
+    "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_SamplingParam,
+    "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_SamplingParam,
+
+    # FastWan2.1
+    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480P_SamplingParam,
+
+    # FastWan2.2
+    "FastVideo/FastWan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
+
+    # Causal Self-Forcing Wan2.1
+    "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers":
+    SelfForcingWan2_1_T2V_1_3B_480P_SamplingParam,
+
+    # Causal Self-Forcing Wan2.2
+    "rand0nmr/SFWan2.2-T2V-A14B-Diffusers":
+    SelfForcingWan2_2_T2V_A14B_480P_SamplingParam,
+    "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers":
+    SelfForcingWan2_2_T2V_A14B_480P_SamplingParam,
+
+    # Cosmos2
+    "nvidia/Cosmos-Predict2-2B-Video2World":
+    Cosmos_Predict2_2B_Video2World_SamplingParam,
+
+    # Cosmos2.5
+    "KyleShao/Cosmos-Predict2.5-2B-Diffusers":
+    Cosmos_Predict2_5_2B_Diffusers_SamplingParam,
+
+    # WanGame models
+    "weizhou03/Wan2.1-Game-Fun-1.3B-InP-Diffusers": MatrixGame2_SamplingParam,
+
+    # MatrixGame2.0 models
+    "FastVideo/Matrix-Game-2.0-Base-Diffusers": MatrixGame2_SamplingParam,
+    "FastVideo/Matrix-Game-2.0-GTA-Diffusers": MatrixGame2_SamplingParam,
+    "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers": MatrixGame2_SamplingParam,
+
+    # TurboDiffusion models
+    "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers":
+    TurboDiffusionT2V_1_3B_SamplingParam,
+    "loayrashid/TurboWan2.1-T2V-14B-Diffusers":
+    TurboDiffusionT2V_14B_SamplingParam,
+    "loayrashid/TurboWan2.2-I2V-A14B-Diffusers":
+    TurboDiffusionI2V_A14B_SamplingParam,
+
+    # LTX-2 models
+    "Lightricks/LTX-2": LTX2SamplingParam,
+    "FastVideo/LTX2-Distilled-Diffusers": LTX2SamplingParam,
+
+    # Add other specific weight variants
+}
+
+# For determining pipeline type from model ID
+SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
+    "hunyuan":
+    lambda id: "hunyuan" in id.lower(),
+    "hunyuan15":
+    lambda id: "hunyuan15" in id.lower(),
+    "hyworld":
+    lambda id: "hyworld" in id.lower(),
+    "wanpipeline":
+    lambda id: "wanpipeline" in id.lower(),
+    "wanimagetovideo":
+    lambda id: "wanimagetovideo" in id.lower(),
+    "stepvideo":
+    lambda id: "stepvideo" in id.lower(),
+    "wandmdpipeline":
+    lambda id: "wandmdpipeline" in id.lower(),
+    "wancausaldmdpipeline":
+    lambda id: "wancausaldmdpipeline" in id.lower(),
+    "wangame":
+    lambda id: "wangame" in id.lower(),
+    "matrixgame":
+    lambda id: "matrixgame" in id.lower() or "matrix-game" in id.lower(),
+    "turbodiffusion":
+    lambda id: "turbodiffusion" in id.lower() or "turbowan" in id.lower(),
+    "cosmos25":
+    lambda id: "cosmos2_5" in id.lower(),
+    "cosmos":
+    lambda id: "cosmos" in id.lower() and "2_5" not in id.lower(),
+    "ltx2":
+    lambda id: "ltx2" in id.lower() or "ltx-2" in id.lower(),
+    # Add other pipeline architecture detectors
+}
+
+# Fallback configs when exact match isn't found but architecture is detected
+SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
+    "hunyuan":
+    HunyuanSamplingParam,  # Base Hunyuan config as fallback for any Hunyuan variant
+    "hunyuan15":
+    Hunyuan15_480P_SamplingParam,  # Base Hunyuan15 config as fallback for any Hunyuan15 variant
+    "hyworld":
+    HYWorld_SamplingParam,  # HYWorld-specific config as fallback for any HYWorld variant
+    "wanpipeline":
+    WanT2V_1_3B_SamplingParam,  # Base Wan config as fallback for any Wan variant
+    "wanimagetovideo": WanI2V_14B_480P_SamplingParam,
+    "wandmdpipeline": FastWanT2V480P_SamplingParam,
+    "wancausaldmdpipeline": SelfForcingWan2_1_T2V_1_3B_480P_SamplingParam,
+    "stepvideo": StepVideoT2VSamplingParam,
+    "wangame": MatrixGame2_SamplingParam,
+    "matrixgame": MatrixGame2_SamplingParam,
+    "turbodiffusion":
+    TurboDiffusionT2V_1_3B_SamplingParam,  # Default to T2V for fallback
+    "cosmos25": Cosmos_Predict2_5_2B_Diffusers_SamplingParam,
+    "cosmos": Cosmos_Predict2_2B_Video2World_SamplingParam,
+    "ltx2": LTX2SamplingParam,
+    # Other fallbacks by architecture
+}
+
+
+def get_sampling_param_cls_for_name(pipeline_name_or_path: str) -> Any | None:
+    """Get the appropriate sampling param for specific pretrained weights."""
+    # First try exact match for specific weights
+    if pipeline_name_or_path in SAMPLING_PARAM_REGISTRY:
+        return SAMPLING_PARAM_REGISTRY[pipeline_name_or_path]
+
+    # Try partial matches (for local paths that might include the weight ID)
+    for registered_id, config_class in SAMPLING_PARAM_REGISTRY.items():
+        if registered_id in pipeline_name_or_path:
+            return config_class
+
+    matrixgame_patterns = ["Matrix-Game", "Skywork--Matrix-Game", "matrixgame"]
+    for pattern in matrixgame_patterns:
+        if pattern.lower() in pipeline_name_or_path.lower():
+            return MatrixGame2_SamplingParam
+
+    if os.path.exists(pipeline_name_or_path):
+        config = verify_model_config_and_directory(pipeline_name_or_path)
+    else:
+        config = maybe_download_model_index(pipeline_name_or_path)
+
+    pipeline_name = config["_class_name"]
+
+    # If no match, try to use the fallback config
+    fallback_config = None
+    # Try to determine pipeline architecture for fallback
+    for pipeline_type, detector in SAMPLING_PARAM_DETECTOR.items():
+        if detector(pipeline_name.lower()):
+            fallback_config = SAMPLING_FALLBACK_PARAM.get(pipeline_type)
+            break
+
+    logger.warning(
+        "No match found for pipeline %s, using fallback sampling param %s.",
+        pipeline_name_or_path, fallback_config)
+    return fallback_config
@@ -148,6 +148,62 @@ class DataValidationStage(DatasetFilterStage):
        return batch


+class ResolutionFilterStage(DatasetFilterStage):
+    """Stage for filtering data items based on resolution constraints."""
+
+    def __init__(self,
+                 max_h_div_w_ratio: float = 17 / 16,
+                 min_h_div_w_ratio: float = 8 / 16,
+                 max_height: int = 1024,
+                 max_width: int = 1024):
+        self.max_h_div_w_ratio = max_h_div_w_ratio
+        self.min_h_div_w_ratio = min_h_div_w_ratio
+        self.max_height = max_height
+        self.max_width = max_width
+
+    def should_keep(self, batch: PreprocessBatch, **kwargs) -> bool:
+        """
+        Check if data item passes resolution filtering.
+        
+        Args:
+            batch: Dataset batch with resolution information
+            
+        Returns:
+            True if passes filter, False otherwise
+        """
+        # Only apply to videos
+        if not batch.is_video:
+            return True
+
+        if batch.resolution is None:
+            return False
+
+        height = batch.resolution.get("height", None)
+        width = batch.resolution.get("width", None)
+        if height is None or width is None:
+            return False
+
+        # Check aspect ratio
+        aspect = self.max_height / self.max_width
+        hw_aspect_thr = 1.5
+
+        return self.filter_resolution(
+            height,
+            width,
+            max_h_div_w_ratio=hw_aspect_thr * aspect,
+            min_h_div_w_ratio=1 / hw_aspect_thr * aspect,
+        )
+
+    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
+        """Process does nothing for resolution filtering - filtering is handled by should_keep."""
+        return batch
+
+    def filter_resolution(self, h: int, w: int, max_h_div_w_ratio: float,
+                          min_h_div_w_ratio: float) -> bool:
+        """Filter based on height/width ratio."""
+        return h / w <= max_h_div_w_ratio and h / w >= min_h_div_w_ratio
+
+
 class FrameSamplingStage(DatasetFilterStage):
    """Stage for temporal frame sampling and indexing."""

@@ -272,6 +328,11 @@ class VideoTransformStage(DatasetStage):
        video = rearrange(video, "t c h w -> c t h w")
        video = video.to(torch.uint8)

+        h, w = video.shape[-2:]
+        assert (
+            h / w <= 17 / 16 and h / w >= 8 / 16
+        ), f"Only videos with a ratio (h/w) less than 17/16 and more than 8/16 are supported. But video ({batch.path}) found ratio is {round(h / w, 2)} with the shape of {video.shape}"
+
        video = video.float() / 127.5 - 1.0
        batch.pixel_values = video
        return batch
@@ -428,6 +489,8 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
                     tokenizer) -> None:
        """Initialize all processing stages."""
        self.validation_stage = DataValidationStage()
+        self.resolution_filter_stage = ResolutionFilterStage(
+            max_height=args.max_height, max_width=args.max_width)
        self.frame_sampling_stage = FrameSamplingStage(
            num_frames=args.num_frames,
            train_fps=args.train_fps,
@@ -480,6 +543,7 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
        # Initialize counters
        filter_counts = {
            "validation_failed": 0,
+            "resolution_failed": 0,
            "frame_sampling_failed": 0
        }
        sample_num_frames: list[int] = []
@@ -515,6 +579,10 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
            filter_counts["validation_failed"] += 1
            return False

+        if not self.resolution_filter_stage.should_keep(batch):
+            filter_counts["resolution_failed"] += 1
+            return False
+
        if not self.frame_sampling_stage.should_keep(batch):
            filter_counts["frame_sampling_failed"] += 1
            return False
@@ -526,9 +594,10 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
                             after_count: int):
        """Log filtering statistics."""
        logger.info(
-            "validation_failed: %d, frame_sampling_failed: %d, "
+            "validation_failed: %d, resolution_failed: %d, frame_sampling_failed: %d, "
            "Counter(sample_num_frames): %s, before filter: %d, after filter: %d",
            filter_counts['validation_failed'],
+            filter_counts['resolution_failed'],
            filter_counts['frame_sampling_failed'], Counter(sample_num_frames),
            before_count, after_count)

@@ -18,7 +18,6 @@ __all__ = [
    "cleanup_dist_env_and_memory",
    "model_parallel_is_initialized",
    "maybe_init_distributed_environment_and_model_parallel",
-    "warmup_sequence_parallel_communication",

    # World group
    "get_world_group",
@@ -7,17 +7,10 @@ import torch.distributed
 from fastvideo.distributed.parallel_state import (get_sp_group,
                                                  get_sp_parallel_rank,
                                                  get_sp_world_size,
-                                                  get_tp_group,
-                                                  model_parallel_is_initialized)
+                                                  get_tp_group)
 from fastvideo.distributed.utils import (unpad_sequence_tensor,
                                         compute_padding_for_sp,
                                         pad_sequence_tensor)
-from fastvideo.logger import init_logger
-
-logger = init_logger(__name__)
-
-# Track if SP communication has been warmed up
-_sp_warmup_done = False


 def tensor_model_parallel_all_reduce(input_: torch.Tensor) -> torch.Tensor:
@@ -108,86 +101,3 @@ def sequence_model_parallel_shard(input_: torch.Tensor,
    input_ = input_.movedim(0, dim)

    return input_, original_seq_len
-
-
-def warmup_sequence_parallel_communication(
-        device: torch.device | None = None) -> None:
-    """Warmup NCCL communicators for sequence parallel all-to-all operations.
-    
-    The first NCCL collective operation is slow due to lazy communicator
-    initialization. This function runs dummy all-to-all operations to
-    trigger the initialization upfront, before the first real forward pass.
-    
-    Args:
-        device: Device to use for warmup tensors. If None, uses CUDA device 0.
-    """
-    global _sp_warmup_done
-
-    if _sp_warmup_done:
-        return
-
-    if not model_parallel_is_initialized():
-        return
-
-    sp_world_size = get_sp_world_size()
-    if sp_world_size <= 1:
-        _sp_warmup_done = True
-        return
-
-    if device is None:
-        device = torch.device("cuda")
-
-    logger.info("Warming up sequence parallel communication (SP=%d)...",
-                sp_world_size)
-
-    # Use small but representative tensor shapes for warmup
-    # Shape: [batch, seq_len, num_heads, head_dim]
-    # The all-to-all patterns used in attention:
-    #   1. scatter_dim=2 (heads), gather_dim=1 (seq) - before attention
-    #   2. scatter_dim=1 (seq), gather_dim=2 (heads) - after attention
-    batch_size = 1
-    seq_len_per_rank = 16  # Small sequence per rank
-    num_heads = sp_world_size * 4  # Must be divisible by sp_world_size
-    head_dim = 64
-
-    # Create dummy tensor for warmup
-    dummy = torch.zeros(batch_size,
-                        seq_len_per_rank,
-                        num_heads,
-                        head_dim,
-                        device=device,
-                        dtype=torch.bfloat16)
-
-    # Warmup pattern 1: scatter heads, gather sequence (before attention)
-    _ = sequence_model_parallel_all_to_all_4D(dummy,
-                                              scatter_dim=2,
-                                              gather_dim=1)
-
-    # Warmup pattern 2: scatter sequence, gather heads (after attention)
-    dummy2 = torch.zeros(batch_size,
-                         seq_len_per_rank * sp_world_size,
-                         num_heads // sp_world_size,
-                         head_dim,
-                         device=device,
-                         dtype=torch.bfloat16)
-    _ = sequence_model_parallel_all_to_all_4D(dummy2,
-                                              scatter_dim=1,
-                                              gather_dim=2)
-
-    # Warmup all-gather (used for replicated tokens)
-    dummy3 = torch.zeros(batch_size,
-                         8,
-                         num_heads // sp_world_size,
-                         head_dim,
-                         device=device,
-                         dtype=torch.bfloat16)
-    _ = sequence_model_parallel_all_gather(dummy3, dim=2)
-
-    # Synchronize to ensure warmup completes
-    torch.cuda.synchronize(device)
-
-    # Clean up
-    del dummy, dummy2, dummy3
-
-    _sp_warmup_done = True
-    logger.info("Sequence parallel communication warmup complete.")
@@ -171,7 +171,7 @@ class StreamingVideoGenerator(VideoGenerator):

    def step(
            self, keyboard_cond: torch.Tensor,
-            mouse_cond: torch.Tensor) -> tuple[list[np.ndarray], Future | None]:
+            mouse_cond: torch.Tensor) -> tuple[list[np.ndarray], Future | None, dict | None]:
        if self.batch is None:
            raise RuntimeError("Call reset() before step()")

@@ -195,7 +195,10 @@ class StreamingVideoGenerator(VideoGenerator):
                # Returns Future for block file, or None if no block_dir
                block_future = self.writer.add_frames(frames)

-        return frames, block_future
+        # Extract stage timings if available
+        stage_timings = getattr(output_batch, 'stage_timings', None)
+
+        return frames, block_future, stage_timings

    async def step_async(
            self, keyboard_cond: torch.Tensor,
@@ -9,7 +9,6 @@ diffusion models.
 import math
 import os
 import re
-import threading
 import time
 from copy import deepcopy
 from typing import Any
@@ -32,21 +31,6 @@ from fastvideo.worker.executor import Executor
 logger = init_logger(__name__)


-def _infer_latent_batch_size(batch: ForwardBatch) -> int:
-    if isinstance(batch.prompt, list):
-        latent_batch_size = len(batch.prompt)
-    elif batch.prompt is not None:
-        latent_batch_size = 1
-    elif batch.prompt_embeds is not None and len(batch.prompt_embeds) > 0:
-        latent_batch_size = batch.prompt_embeds[0].shape[0]
-    else:
-        raise ValueError(
-            "Cannot infer batch size from batch; no prompt or prompt_embeds found"
-        )
-    latent_batch_size *= batch.num_videos_per_prompt
-    return latent_batch_size
-
-
 class VideoGenerator:
    """
    A unified class for generating videos using diffusion models.
@@ -388,31 +372,8 @@ class VideoGenerator:

        # Run inference
        start_time = time.perf_counter()
-
-        # Execute forward pass in a new thread for non-blocking tensor allocation
-        result_container = {}
-
-        def execute_forward_thread():
-            result_container['output_batch'] = self.executor.execute_forward(
-                batch, fastvideo_args)
-
-        thread = threading.Thread(target=execute_forward_thread)
-        thread.start()
-        latent_batch_size = _infer_latent_batch_size(batch)
-        samples = torch.empty((latent_batch_size, 3, sampling_param.num_frames,
-                               sampling_param.height, sampling_param.width),
-                              device='cpu',
-                              pin_memory=fastvideo_args.pin_cpu_memory)
-        thread.join()
-
-        output_batch = result_container['output_batch']
-        if output_batch.output.shape == samples.shape:
-            samples.copy_(output_batch.output)
-        else:
-            logger.warning(
-                "Output shape %s does not match expected shape %s; use slow path",
-                output_batch.output.shape, samples.shape)
-            samples = output_batch.output.cpu()
+        output_batch = self.executor.execute_forward(batch, fastvideo_args)
+        samples = output_batch.output
        logging_info = output_batch.logging_info

        gen_time = time.perf_counter() - start_time
@@ -10,7 +10,7 @@ from enum import Enum
 from typing import Any, TYPE_CHECKING

 from fastvideo.configs.configs import PreprocessConfig
-from fastvideo.configs.pipelines.base import PipelineConfig
+from fastvideo.configs.pipelines.base import PipelineConfig, STA_Mode
 from fastvideo.configs.utils import clean_cli_args
 from fastvideo.layers.quantization import QUANTIZATION_METHODS, QuantizationMethods
 from fastvideo.logger import init_logger
@@ -141,6 +141,8 @@ class FastVideoArgs:

    # STA (Sliding Tile Attention) parameters
    mask_strategy_file_path: str | None = None
+    STA_mode: STA_Mode = STA_Mode.STA_INFERENCE
+    skip_time_steps: int = 15

    # Compilation
    enable_torch_compile: bool = False
@@ -177,7 +179,6 @@ class FastVideoArgs:
    model_loaded: dict[str, bool] = field(default_factory=lambda: {
        "transformer": True,
        "vae": True,
-        "upsampler": True,
    })

    override_text_encoder_safetensors: str | None = None  # path to safetensors file for text encoder override
@@ -463,6 +464,20 @@ class FastVideoArgs:
        )

        # STA (Sliding Tile Attention) parameters
+        parser.add_argument(
+            "--STA-mode",
+            type=str,
+            default=FastVideoArgs.STA_mode.value,
+            choices=[mode.value for mode in STA_Mode],
+            help=
+            "STA mode contains STA_inference, STA_searching, STA_tuning, STA_tuning_cfg, None",
+        )
+        parser.add_argument(
+            "--skip-time-steps",
+            type=int,
+            default=FastVideoArgs.skip_time_steps,
+            help="Number of time steps to warmup (full attention) for STA",
+        )
        parser.add_argument(
            "--mask-strategy-file-path",
            type=str,
@@ -916,7 +931,6 @@ class TrainingArgs(FastVideoArgs):
    training_state_checkpointing_steps: int = 0  # for resuming training
    weight_only_checkpointing_steps: int = 0  # for inference
    log_visualization: bool = False
-    visualization_steps: int = 0
    # simulate generator forward to match inference
    simulate_generator_forward: bool = False
    warp_denoising_step: bool = False
@@ -1080,9 +1094,6 @@ class TrainingArgs(FastVideoArgs):
        parser.add_argument("--log-validation",
                            action=StoreBoolean,
                            help="Whether to log validation results")
-        parser.add_argument("--visualization-steps",
-                            type=int,
-                            help="Number of visualization steps")
        parser.add_argument("--tracker-project-name",
                            type=str,
                            help="Project name for tracking")
@@ -193,46 +193,3 @@ class ImageProcessor:
        tensor = 2.0 * tensor - 1.0

        return tensor
-
-    def _preprocess_cosmos25(
-        self,
-        image: PIL.Image.Image | np.ndarray | torch.Tensor,
-        height: int,
-        width: int,
-    ) -> torch.Tensor:
-        """
-        Cosmos-Predict2.5-style preprocessing for image2world:
-        - aspect-preserving resize (scale so both dims >= target)
-        - center crop to (height, width)
-        - normalize to [-1, 1]
-
-        Returns:
-            torch.Tensor: (1, 3, height, width) in [-1, 1]
-        """
-        if not isinstance(image, PIL.Image.Image):
-            # Reuse the generic preprocess for non-PIL inputs.
-            return self.preprocess(image, height=height, width=width)
-
-        # Ensure RGB (official uses torchvision.to_tensor on PIL, which yields C=3 for RGB inputs)
-        image = image.convert("RGB")
-
-        # Make sure target dims are multiples of VAE scale factor (FastVideo convention)
-        height = height - (height % self.vae_scale_factor)
-        width = width - (width % self.vae_scale_factor)
-
-        orig_w, orig_h = image.size
-        # Scale so that resized image fully covers the target box (like official resize_input()).
-        scale = max(width / float(orig_w), height / float(orig_h))
-        resized_w = int(np.ceil(scale * orig_w))
-        resized_h = int(np.ceil(scale * orig_h))
-
-        image = image.resize((resized_w, resized_h),
-                             resample=PIL.Image.Resampling.BILINEAR)
-
-        # Center crop
-        left = max(0, (resized_w - width) // 2)
-        top = max(0, (resized_h - height) // 2)
-        image = image.crop((left, top, left + width, top + height))
-
-        image_np = np.array(image, dtype=np.float32) / 255.0  # [0,1]
-        return self._normalize_to_tensor(image_np)  # -> [-1,1], (1,3,H,W)
@@ -27,7 +27,7 @@ RESET = '\033[0;0m'
 _warned_local_main_process = False
 _warned_main_process = False

-_FORMAT = (f"{FASTVIDEO_LOGGING_PREFIX}%(levelname)s %(asctime)s.%(msecs)03d "
+_FORMAT = (f"{FASTVIDEO_LOGGING_PREFIX}%(levelname)s %(asctime)s "
           "[%(filename)s:%(lineno)d] %(message)s")
 _DATE_FORMAT = "%m-%d %H:%M:%S"

@@ -102,7 +102,6 @@ def _info(logger: Logger,
        - When both are False, the message will be logged from all processes
        - By default, only logs from processes with LOCAL_RANK=0
    """
-    is_distributed = int(os.environ.get("WORLD_SIZE", 1)) > 1
    try:
        local_rank = int(os.environ["LOCAL_RANK"])
        rank = int(os.environ["RANK"])
@@ -119,22 +118,20 @@ def _info(logger: Logger,

    global _warned_local_main_process, _warned_main_process

-    # Only show process-awareness warnings when actually running distributed
-    if is_distributed:
-        if not _warned_local_main_process and local_main_process_only:
-            logger.warning(
-                '%s By default, logger.info(..) will only log from the local main process. Set logger.info(..., is_local_main_process=False) to log from all processes.%s',
-                GREEN,
-                RESET,
-            )
-            _warned_local_main_process = True
-        if not _warned_main_process and main_process_only:
-            logger.warning(
-                '%s is_main_process_only is set to True, logging only from the main (RANK==0) process.%s',
-                GREEN,
-                RESET,
-            )
-            _warned_main_process = True
+    if not _warned_local_main_process and local_main_process_only:
+        logger.warning(
+            '%s By default, logger.info(..) will only log from the local main process. Set logger.info(..., is_local_main_process=False) to log from all processes.%s',
+            GREEN,
+            RESET,
+        )
+        _warned_local_main_process = True
+    if not _warned_main_process and main_process_only:
+        logger.warning(
+            '%s is_main_process_only is set to True, logging only from the main (RANK==0) process.%s',
+            GREEN,
+            RESET,
+        )
+        _warned_main_process = True

    if not main_process_only and not local_main_process_only:
        logger.log(logging.INFO, msg, *args, stacklevel=2, **kwargs)
@@ -1325,6 +1325,3 @@ def decode_audio(
    decoded_audio = audio_decoder(latent)
    decoded_audio = vocoder(decoded_audio).squeeze(0).float()
    return decoded_audio
-
-# Entry point for model registry
-EntryClass = [LTX2AudioEncoder, LTX2AudioDecoder, LTX2Vocoder]
@@ -33,7 +33,7 @@ from fastvideo.layers.visual_embedding import (PatchEmbed)
 from fastvideo.logger import init_logger
 from fastvideo.models.dits.base import BaseDiT
 from fastvideo.models.dits.wanvideo import WanT2VCrossAttention, WanTimeTextImageEmbedding
-from fastvideo.platforms import AttentionBackendEnum, current_platform
+from fastvideo.platforms import AttentionBackendEnum

 logger = init_logger(__name__)
 class CausalWanSelfAttention(nn.Module):
@@ -286,8 +286,6 @@ class CausalWanTransformerBlock(nn.Module):
        null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
        norm_hidden_states, hidden_states = self.self_attn_residual_norm(
            hidden_states, attn_output, gate_msa, null_shift, null_scale)
-        norm_hidden_states, hidden_states = norm_hidden_states.to(
-            orig_dtype), hidden_states.to(orig_dtype)

        # 2. Cross-attention
        attn_output = self.attn2(norm_hidden_states,
@@ -454,6 +452,7 @@ class CausalWanTransformer3DModel(BaseDiT):
        This function will be run for num_frame times.
        Process the latent frames one by one (1560 tokens each)
        """
+        from fastvideo.platforms import current_platform

        orig_dtype = hidden_states.dtype
        if not isinstance(encoder_hidden_states, torch.Tensor):
@@ -677,7 +676,4 @@ class CausalWanTransformer3DModel(BaseDiT):
            # u = torch.einsum('fhwpqrc->cfphqwr', u.contiguous())
            u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
            out.append(u)
-        return out
-
-# Entry point for model registry
-EntryClass = CausalWanTransformer3DModel
+        return out
@@ -723,7 +723,4 @@ class CosmosTransformer3DModel(BaseDiT):
        hidden_states = hidden_states.permute(0, 7, 1, 6, 2, 4, 3, 5)
        hidden_states = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)

-        return hidden_states
-
-# Entry point for model registry
-EntryClass = CosmosTransformer3DModel
+        return hidden_states
@@ -959,5 +959,3 @@ class Cosmos25Transformer3DModel(BaseDiT):

        return hidden_states

-# Entry point for model registry
-EntryClass = Cosmos25Transformer3DModel
@@ -940,6 +940,3 @@ class FinalLayer(nn.Module):
        x = self.norm_final(x) * (1.0 + scale.unsqueeze(1)) + shift.unsqueeze(1)
        x, _ = self.linear(x)
        return x
-
-# Entry point for model registry
-EntryClass = HunyuanVideoTransformer3DModel
@@ -852,7 +852,4 @@ class FinalLayer(nn.Module):
        scale, shift = self.adaLN_modulation(c).chunk(2, dim=-1)
        x = self.norm_final(x) * (1.0 + scale.unsqueeze(1)) + shift.unsqueeze(1)
        x, _ = self.linear(x)
-        return x
-
-# Entry point for model registry
-EntryClass = HunyuanVideo15Transformer3DModel
+        return x
@@ -22,6 +22,3 @@ __all__ = [
    # Inference utilities (used by examples)
    "get_resolution_from_image",
 ]
-
-# Entry point for model registry
-EntryClass = HYWorldTransformer3DModel
@@ -536,8 +536,8 @@ class HYWorldTransformer3DModel(HunyuanVideo15Transformer3DModel):
                    temb_txt,
                    freqs_cis,
                    seq_attention_mask,
-                    viewmats_seq,
-                    Ks_seq,
+                    viewmats_seq, # hyworld
+                    Ks_seq, # hyworld
                )
        else:
            for block in self.double_blocks:
@@ -549,8 +549,8 @@ class HYWorldTransformer3DModel(HunyuanVideo15Transformer3DModel):
                    temb_txt,
                    freqs_cis,
                    seq_attention_mask,
-                    viewmats_seq,
-                    Ks_seq,
+                    viewmats=viewmats_seq, # hyworld
+                    Ks=Ks_seq, # hyworld
                )


@@ -1134,5 +1134,3 @@ class LongCatTransformer3DModel(CachableDiT):
        )
        return x

-# Entry point for model registry
-EntryClass = LongCatTransformer3DModel
@@ -18,23 +18,12 @@ import torch.nn as nn
 from einops import rearrange, repeat

 from fastvideo.attention.backends.sdpa import SDPAMetadata
-from fastvideo.attention.layer import DistributedAttention, LocalAttention
+from fastvideo.attention.layer import LocalAttention
 from fastvideo.configs.models.dits import LTX2VideoConfig
-from fastvideo.distributed.communication_op import (
-    sequence_model_parallel_all_gather,
-    sequence_model_parallel_all_gather_with_unpad,
-    sequence_model_parallel_all_to_all_4D,
-    sequence_model_parallel_shard,
-)
-from fastvideo.distributed.parallel_state import get_sp_parallel_rank, get_sp_world_size
-from fastvideo.distributed.utils import create_attention_mask_for_padding
-from fastvideo.forward_context import ForwardContext, get_forward_context, set_forward_context
-from fastvideo.logger import init_logger
+from fastvideo.forward_context import get_forward_context, set_forward_context
 from fastvideo.models.dits.base import CachableDiT
 from fastvideo.platforms import AttentionBackendEnum

-logger = init_logger(__name__)
-

 def get_timestep_embedding(
    timesteps: torch.Tensor,
@@ -306,12 +295,6 @@ class VideoLatentPatchifier:
        output_shape: VideoLatentShape,
        device: Optional[torch.device] = None,
    ) -> torch.Tensor:
-        """Get patch grid bounds for RoPE computation.
-
-        Args:
-            output_shape: Shape of the video latent tensor
-            device: Device to create tensors on
-        """
        frames = output_shape.frames
        height = output_shape.height
        width = output_shape.width
@@ -383,12 +366,6 @@ class AudioLatentPatchifier:
        output_shape: AudioLatentShape,
        device: Optional[torch.device] = None,
    ) -> torch.Tensor:
-        """Get patch grid bounds for audio RoPE computation.
-
-        Args:
-            output_shape: Shape of the audio latent tensor
-            device: Device to create tensors on
-        """
        start_timings = self._get_audio_latent_time_in_sec(
            self.shift,
            output_shape.frames + self.shift,
@@ -538,44 +515,6 @@ def apply_ltx_rotary_emb(
    raise ValueError(f"Invalid rope type: {rope_type}")


-def apply_ltx_rotary_emb_4d(
-    input_tensor: torch.Tensor,
-    freqs_cis: tuple[torch.Tensor, torch.Tensor],
-    rope_type: LTXRopeType = LTXRopeType.INTERLEAVED,
-) -> torch.Tensor:
-    """Apply LTX-2 rotary embeddings to a 4D tensor [B, H, T, D].
-    
-    This is used for applying RoPE after all-to-all in distributed attention,
-    where the tensor is already in [B, H, T, D] format.
-    """
-    cos_freqs, sin_freqs = freqs_cis
-    if rope_type == LTXRopeType.INTERLEAVED:
-        # For interleaved, cos/sin have shape [B, T, inner_dim]
-        # Need to reshape to [B, H, T, D] format
-        # Actually interleaved doesn't have per-head rotations, so we broadcast
-        t_dup = rearrange(input_tensor, "... (d r) -> ... d r", r=2)
-        t1, t2 = t_dup.unbind(dim=-1)
-        t_dup = torch.stack((-t2, t1), dim=-1)
-        input_tensor_rot = rearrange(t_dup, "... d r -> ... (d r)")
-        return input_tensor * cos_freqs + input_tensor_rot * sin_freqs
-    if rope_type == LTXRopeType.SPLIT:
-        # For split, cos/sin already have shape [B, H, T, D/2]
-        # input_tensor is [B, H, T, D]
-        split_input = rearrange(input_tensor, "... (d r) -> ... d r", d=2)
-        first_half_input = split_input[..., :1, :]
-        second_half_input = split_input[..., 1:, :]
-
-        output = split_input * cos_freqs.unsqueeze(-2)
-        first_half_output = output[..., :1, :]
-        second_half_output = output[..., 1:, :]
-
-        first_half_output.addcmul_(-sin_freqs.unsqueeze(-2), second_half_input)
-        second_half_output.addcmul_(sin_freqs.unsqueeze(-2), first_half_input)
-
-        return rearrange(output, "... d r -> ... (d r)")
-    raise ValueError(f"Invalid rope type: {rope_type}")
-
-
 def _apply_ltx_interleaved_rotary_emb(
    input_tensor: torch.Tensor, cos_freqs: torch.Tensor, sin_freqs: torch.Tensor
 ) -> torch.Tensor:
@@ -979,205 +918,6 @@ class TransformerConfig:
    context_dim: int


-class LTXDistributedAttention(DistributedAttention):
-    """LTX-2 specialized DistributedAttention that handles LTX-style RoPE internally."""
-
-    def __init__(
-        self,
-        num_heads: int,
-        head_size: int,
-        rope_type: LTXRopeType,
-        num_kv_heads: int | None = None,
-        softmax_scale: float | None = None,
-        causal: bool = False,
-        supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
-        prefix: str = "",
-        **extra_impl_args,
-    ) -> None:
-        super().__init__(
-            num_heads=num_heads,
-            head_size=head_size,
-            num_kv_heads=num_kv_heads,
-            softmax_scale=softmax_scale,
-            causal=causal,
-            supported_attention_backends=supported_attention_backends,
-            prefix=prefix,
-            **extra_impl_args,
-        )
-        self.rope_type = rope_type
-
-    @torch.compiler.disable
-    def forward(
-        self,
-        q: torch.Tensor,
-        k: torch.Tensor,
-        v: torch.Tensor,
-        replicated_q: torch.Tensor | None = None,
-        replicated_k: torch.Tensor | None = None,
-        replicated_v: torch.Tensor | None = None,
-        attention_mask: torch.Tensor | None = None,
-        ltx_freqs_cis: tuple[torch.Tensor, torch.Tensor] | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor | None]:
-        """Forward pass with LTX-2 style RoPE application.
-
-        Args:
-            q: Query tensor [batch_size, seq_len, num_heads, head_dim]
-            k: Key tensor [batch_size, seq_len, num_heads, head_dim]
-            v: Value tensor [batch_size, seq_len, num_heads, head_dim]
-            replicated_q: Replicated query tensor for text tokens
-            replicated_k: Replicated key tensor
-            replicated_v: Replicated value tensor
-            attention_mask: Attention mask [batch_size, seq_len]
-            ltx_freqs_cis: LTX-2 style RoPE (cos, sin) with shape [B, H, T, D]
-
-        Returns:
-            Tuple of output tensor and optional replicated output
-        """
-        assert q.dim() == 4 and k.dim() == 4 and v.dim() == 4, "Expected 4D tensors"
-        batch_size, seq_len, num_heads, head_dim = q.shape
-        local_rank = get_sp_parallel_rank()
-        world_size = get_sp_world_size()
-
-        forward_context: ForwardContext = get_forward_context()
-        ctx_attn_metadata = forward_context.attn_metadata
-
-        # Stack QKV
-        qkv = torch.cat([q, k, v], dim=0)  # [3*batch, seq_len, num_heads, head_dim]
-
-        # Redistribute heads across sequence dimension
-        qkv = sequence_model_parallel_all_to_all_4D(qkv, scatter_dim=2, gather_dim=1)
-
-        # After all-to-all, each rank has the full sequence but only a subset of heads
-        valid_seq_len = None
-        if attention_mask is not None:
-            valid_seq_len = (attention_mask[0] == 1).sum().item()
-            qkv = qkv[:, :valid_seq_len, :, :]
-
-        # Apply LTX-2 style RoPE after all-to-all (when we have full sequence)
-        if ltx_freqs_cis is not None:
-            cos, sin = ltx_freqs_cis
-            heads_per_rank = num_heads // world_size
-            head_start = local_rank * heads_per_rank
-            head_end = head_start + heads_per_rank
-            # Slice to this rank's heads: [B, H/SP, T, D]
-            cos_local = cos[:, head_start:head_end, :valid_seq_len, :]
-            sin_local = sin[:, head_start:head_end, :valid_seq_len, :]
-
-            # Apply RoPE to Q and K together (first 2*batch_size in dim 0)
-            qk_part = qkv[:batch_size * 2]
-            # Transpose to [2*B, H/SP, T, D] for LTX RoPE application
-            qk_part = qk_part.transpose(1, 2)
-            qk_part = apply_ltx_rotary_emb_4d(qk_part, (cos_local, sin_local), self.rope_type)
-            # Transpose back to [2*B, T, H/SP, D]
-            qkv[:batch_size * 2] = qk_part.transpose(1, 2)
-
-        # Apply backend-specific preprocess_qkv
-        qkv = self.attn_impl.preprocess_qkv(qkv, ctx_attn_metadata)
-
-        # Concatenate with replicated QKV if provided
-        if replicated_q is not None:
-            assert replicated_k is not None and replicated_v is not None
-            replicated_qkv = torch.cat(
-                [replicated_q, replicated_k, replicated_v],
-                dim=0)  # [3, seq_len, num_heads, head_dim]
-            heads_per_rank = num_heads // world_size
-            replicated_qkv = replicated_qkv[:, :, local_rank *
-                                            heads_per_rank:(local_rank + 1) *
-                                            heads_per_rank]
-            qkv = torch.cat([qkv, replicated_qkv], dim=1)
-
-        q, k, v = qkv.chunk(3, dim=0)
-
-        output = self.attn_impl.forward(q, k, v, ctx_attn_metadata)
-
-        # Redistribute back if using sequence parallelism
-        replicated_output = None
-        if replicated_q is not None:
-            split_idx = seq_len * world_size if valid_seq_len is None else valid_seq_len
-            replicated_output = output[:, split_idx:]
-            output = output[:, :split_idx]
-            replicated_output = sequence_model_parallel_all_gather(
-                replicated_output.contiguous(), dim=2)
-
-        # Apply backend-specific postprocess_output
-        output = self.attn_impl.postprocess_output(output, ctx_attn_metadata)
-
-        if attention_mask is not None:
-            pad_len = (attention_mask[0] == 0).sum().item()
-            output = torch.nn.functional.pad(output, (0, 0, 0, 0, 0, pad_len))
-
-        output = sequence_model_parallel_all_to_all_4D(output, scatter_dim=1, gather_dim=2)
-
-        return output, replicated_output
-
-
-class LTXLocalAttention(LocalAttention):
-    """LTX-2 specialized LocalAttention that handles LTX-style RoPE internally."""
-
-    def __init__(
-        self,
-        num_heads: int,
-        head_size: int,
-        rope_type: LTXRopeType,
-        num_kv_heads: int | None = None,
-        softmax_scale: float | None = None,
-        causal: bool = False,
-        supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
-        **extra_impl_args,
-    ) -> None:
-        super().__init__(
-            num_heads=num_heads,
-            head_size=head_size,
-            num_kv_heads=num_kv_heads,
-            softmax_scale=softmax_scale,
-            causal=causal,
-            supported_attention_backends=supported_attention_backends,
-            **extra_impl_args,
-        )
-        self.rope_type = rope_type
-
-    def forward(
-        self,
-        q: torch.Tensor,
-        k: torch.Tensor,
-        v: torch.Tensor,
-        ltx_freqs_cis: tuple[torch.Tensor, torch.Tensor] | None = None,
-        ltx_k_freqs_cis: tuple[torch.Tensor, torch.Tensor] | None = None,
-    ) -> torch.Tensor:
-        """Forward pass with LTX-2 style RoPE application.
-
-        Args:
-            q: Query tensor [batch_size, seq_len, num_heads, head_dim]
-            k: Key tensor [batch_size, seq_len, num_heads, head_dim]
-            v: Value tensor [batch_size, seq_len, num_heads, head_dim]
-            ltx_freqs_cis: LTX-2 style RoPE (cos, sin) for Q with shape [B, H, T, D]
-            ltx_k_freqs_cis: LTX-2 style RoPE (cos, sin) for K (if different from Q)
-
-        Returns:
-            Output tensor after local attention
-        """
-        assert q.dim() == 4 and k.dim() == 4 and v.dim() == 4, "Expected 4D tensors"
-
-        forward_context: ForwardContext = get_forward_context()
-        ctx_attn_metadata = forward_context.attn_metadata
-
-        # Apply LTX-2 style RoPE
-        if ltx_freqs_cis is not None:
-            # Use separate K RoPE if provided (for cross-attention), otherwise use same as Q
-            k_freqs = ltx_k_freqs_cis if ltx_k_freqs_cis is not None else ltx_freqs_cis
-            # Transpose to [B, H, T, D] for RoPE application
-            q = q.transpose(1, 2)
-            k = k.transpose(1, 2)
-            q = apply_ltx_rotary_emb_4d(q, ltx_freqs_cis, self.rope_type)
-            k = apply_ltx_rotary_emb_4d(k, k_freqs, self.rope_type)
-            # Transpose back to [B, T, H, D]
-            q = q.transpose(1, 2)
-            k = k.transpose(1, 2)
-
-        output = self.attn_impl.forward(q, k, v, ctx_attn_metadata)
-        return output
-
-
 class LTXSelfAttention(nn.Module):
    """LTX-2 attention block with RMSNorm + FastVideo LocalAttention."""
    def __init__(
@@ -1205,19 +945,17 @@ class LTXSelfAttention(nn.Module):
        self.to_v = nn.Linear(context_dim, inner_dim, bias=True)
        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim, bias=True), nn.Identity())

-        self.attn = LTXLocalAttention(
+        self.attn = LocalAttention(
            num_heads=heads,
            head_size=dim_head,
-            rope_type=rope_type,
            dropout_rate=0,
            softmax_scale=None,
            causal=False,
            supported_attention_backends=supported_attention_backends,
        )
-        self.attn_masked = LTXLocalAttention(
+        self.attn_masked = LocalAttention(
            num_heads=heads,
            head_size=dim_head,
-            rope_type=rope_type,
            dropout_rate=0,
            softmax_scale=None,
            causal=False,
@@ -1240,13 +978,15 @@ class LTXSelfAttention(nn.Module):
        q = self.q_norm(q)
        k = self.k_norm(k)

-        # RoPE is applied inside LTXLocalAttention
+        if pe is not None:
+            q = apply_ltx_rotary_emb(q, pe, self.rope_type)
+            k = apply_ltx_rotary_emb(k, pe if k_pe is None else k_pe, self.rope_type)
+
        b, q_len, _ = q.shape
        k_len = k.shape[1]
        q = q.view(b, q_len, self.heads, self.dim_head)
        k = k.view(b, k_len, self.heads, self.dim_head)
        v = v.view(b, k_len, self.heads, self.dim_head)
-
        if mask is not None:
            if mask.ndim == 2:
                mask = mask.unsqueeze(0)
@@ -1268,93 +1008,9 @@ class LTXSelfAttention(nn.Module):
                attn_metadata=attn_metadata,
                forward_batch=forward_batch,
            ):
-                out = self.attn_masked(q, k, v, ltx_freqs_cis=pe, ltx_k_freqs_cis=k_pe)
+                out = self.attn_masked(q, k, v)
        else:
-            out = self.attn(q, k, v, ltx_freqs_cis=pe, ltx_k_freqs_cis=k_pe)
-        out = out.reshape(b, q_len, -1)
-        return self.to_out(out)
-
-
-class LTXDistributedSelfAttention(nn.Module):
-    """LTX-2 attention block with RMSNorm + LTXDistributedAttention for SP."""
-
-    def __init__(
-        self,
-        query_dim: int,
-        context_dim: int | None,
-        heads: int,
-        dim_head: int,
-        norm_eps: float,
-        rope_type: LTXRopeType,
-        supported_attention_backends: tuple[AttentionBackendEnum, ...],
-        prefix: str = "",
-    ) -> None:
-        super().__init__()
-        inner_dim = dim_head * heads
-        context_dim = query_dim if context_dim is None else context_dim
-
-        self.heads = heads
-        self.dim_head = dim_head
-        self.rope_type = rope_type
-
-        self.q_norm = torch.nn.RMSNorm(inner_dim, eps=norm_eps)
-        self.k_norm = torch.nn.RMSNorm(inner_dim, eps=norm_eps)
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=True)
-        self.to_k = nn.Linear(context_dim, inner_dim, bias=True)
-        self.to_v = nn.Linear(context_dim, inner_dim, bias=True)
-        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim, bias=True), nn.Identity())
-
-        self.attn = LTXDistributedAttention(
-            num_heads=heads,
-            head_size=dim_head,
-            rope_type=rope_type,
-            causal=False,
-            supported_attention_backends=supported_attention_backends,
-            prefix=f"{prefix}.attn",
-        )
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        context: torch.Tensor | None = None,
-        pe: tuple[torch.Tensor, torch.Tensor] | None = None,
-        k_pe: tuple[torch.Tensor, torch.Tensor] | None = None,
-        attention_mask: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        """Forward pass for distributed self-attention.
-
-        Args:
-            x: Query tensor [B, L, C]
-            context: Key/Value tensor [B, L, C], or None for self-attention
-            pe: Rotary position embeddings for Q (cos, sin) with shape [B, H, T_full, D]
-                NOTE: For SP, this must be the FULL sequence RoPE, not sharded!
-            k_pe: Rotary position embeddings for K (cos, sin), or None to use pe
-            attention_mask: Attention mask for padding [B, padded_seq_len]
-        """
-        q = self.to_q(x)
-        context = x if context is None else context
-        k = self.to_k(context)
-        v = self.to_v(context)
-
-        q = self.q_norm(q)
-        k = self.k_norm(k)
-
-        # RoPE is applied inside LTXDistributedAttention AFTER the all-to-all,
-        # when each rank has the full sequence (but subset of heads).
-        b, q_len, _ = q.shape
-        k_len = k.shape[1]
-        q = q.view(b, q_len, self.heads, self.dim_head)
-        k = k.view(b, k_len, self.heads, self.dim_head)
-        v = v.view(b, k_len, self.heads, self.dim_head)
-
-        # Pass full RoPE to distributed attention - it will apply after all-to-all
-        # and slice to this rank's heads
-        out, _ = self.attn(
-            q, k, v,
-            attention_mask=attention_mask,
-            ltx_freqs_cis=pe,
-        )
-
+            out = self.attn(q, k, v)
        out = out.reshape(b, q_len, -1)
        return self.to_out(out)

@@ -1369,31 +1025,12 @@ class BasicAVTransformerBlock(torch.nn.Module):
        audio: TransformerConfig | None = None,
        rope_type: LTXRopeType = LTXRopeType.INTERLEAVED,
        norm_eps: float = 1e-6,
-        use_distributed_attention: bool = False,
-        prefix: str = "",
    ):
        super().__init__()
        self.idx = idx
-        self.use_distributed_attention = use_distributed_attention
-
-        # Choose attention class based on SP mode
-        # Self-attention and audio-video cross-attention use DistributedAttention when SP > 1
-        # Text cross-attention uses LocalAttention (text embeddings are replicated)
-        SelfAttnCls = LTXDistributedSelfAttention if use_distributed_attention else LTXSelfAttention
-        CrossAttnCls = LTXSelfAttention  # Text cross-attention is always local

        if video is not None:
-            # Video self-attention - use distributed when SP > 1
-            self.attn1 = SelfAttnCls(
-                query_dim=video.dim,
-                context_dim=None,
-                heads=video.heads,
-                dim_head=video.d_head,
-                norm_eps=norm_eps,
-                rope_type=rope_type,
-                supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA),
-                prefix=f"{prefix}.blocks.{idx}.attn1" if use_distributed_attention else "",
-            ) if use_distributed_attention else LTXSelfAttention(
+            self.attn1 = LTXSelfAttention(
                query_dim=video.dim,
                context_dim=None,
                heads=video.heads,
@@ -1402,8 +1039,7 @@ class BasicAVTransformerBlock(torch.nn.Module):
                rope_type=rope_type,
                supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA),
            )
-            # Text cross-attention - always local (text is replicated)
-            self.attn2 = CrossAttnCls(
+            self.attn2 = LTXSelfAttention(
                query_dim=video.dim,
                context_dim=video.context_dim,
                heads=video.heads,
@@ -1416,17 +1052,7 @@ class BasicAVTransformerBlock(torch.nn.Module):
            self.scale_shift_table = torch.nn.Parameter(torch.empty(6, video.dim))

        if audio is not None:
-            # Audio self-attention - use distributed when SP > 1
-            self.audio_attn1 = SelfAttnCls(
-                query_dim=audio.dim,
-                context_dim=None,
-                heads=audio.heads,
-                dim_head=audio.d_head,
-                norm_eps=norm_eps,
-                rope_type=rope_type,
-                supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA),
-                prefix=f"{prefix}.blocks.{idx}.audio_attn1" if use_distributed_attention else "",
-            ) if use_distributed_attention else LTXSelfAttention(
+            self.audio_attn1 = LTXSelfAttention(
                query_dim=audio.dim,
                context_dim=None,
                heads=audio.heads,
@@ -1435,8 +1061,7 @@ class BasicAVTransformerBlock(torch.nn.Module):
                rope_type=rope_type,
                supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA),
            )
-            # Text cross-attention - always local (text is replicated)
-            self.audio_attn2 = CrossAttnCls(
+            self.audio_attn2 = LTXSelfAttention(
                query_dim=audio.dim,
                context_dim=audio.context_dim,
                heads=audio.heads,
@@ -1449,8 +1074,6 @@ class BasicAVTransformerBlock(torch.nn.Module):
            self.audio_scale_shift_table = torch.nn.Parameter(torch.empty(6, audio.dim))

        if audio is not None and video is not None:
-            # Audio-to-video cross-attention
-            # Uses local attention - context is gathered from all SP ranks in forward()
            self.audio_to_video_attn = LTXSelfAttention(
                query_dim=video.dim,
                context_dim=audio.dim,
@@ -1460,8 +1083,6 @@ class BasicAVTransformerBlock(torch.nn.Module):
                rope_type=rope_type,
                supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA),
            )
-            # Video-to-audio cross-attention
-            # Uses local attention - context is gathered from all SP ranks in forward()
            self.video_to_audio_attn = LTXSelfAttention(
                query_dim=audio.dim,
                context_dim=video.dim,
@@ -1510,17 +1131,7 @@ class BasicAVTransformerBlock(torch.nn.Module):
        self,
        video: TransformerArgs | None,
        audio: TransformerArgs | None,
-        video_attention_mask: torch.Tensor | None = None,
-        audio_attention_mask: torch.Tensor | None = None,
    ) -> tuple[TransformerArgs | None, TransformerArgs | None]:
-        """Forward pass for transformer block.
-
-        Args:
-            video: Video transformer args
-            audio: Audio transformer args
-            video_attention_mask: SP padding attention mask for video [B, padded_seq_len]
-            audio_attention_mask: SP padding attention mask for audio [B, padded_seq_len]
-        """
        vx = video.x if video is not None else None
        ax = audio.x if audio is not None else None

@@ -1534,12 +1145,7 @@ class BasicAVTransformerBlock(torch.nn.Module):
                self.scale_shift_table, vx.shape[0], video.timesteps, slice(0, 3)
            )
            norm_vx = torch.nn.functional.rms_norm(vx, (vx.shape[-1],), eps=self.norm_eps) * (1 + vscale_msa) + vshift_msa
-            # Self-attention: pass SP attention mask for distributed attention
-            if self.use_distributed_attention:
-                vx = vx + self.attn1(norm_vx, pe=video.positional_embeddings, attention_mask=video_attention_mask) * vgate_msa
-            else:
-                vx = vx + self.attn1(norm_vx, pe=video.positional_embeddings) * vgate_msa
-            # Text cross-attention: no SP mask needed (text is replicated, uses local attention)
+            vx = vx + self.attn1(norm_vx, pe=video.positional_embeddings) * vgate_msa
            vx = vx + self.attn2(
                torch.nn.functional.rms_norm(vx, (vx.shape[-1],), eps=self.norm_eps),
                context=video.context,
@@ -1551,12 +1157,7 @@ class BasicAVTransformerBlock(torch.nn.Module):
                self.audio_scale_shift_table, ax.shape[0], audio.timesteps, slice(0, 3)
            )
            norm_ax = torch.nn.functional.rms_norm(ax, (ax.shape[-1],), eps=self.norm_eps) * (1 + ascale_msa) + ashift_msa
-            # Self-attention: pass SP attention mask for distributed attention
-            if self.use_distributed_attention:
-                ax = ax + self.audio_attn1(norm_ax, pe=audio.positional_embeddings, attention_mask=audio_attention_mask) * agate_msa
-            else:
-                ax = ax + self.audio_attn1(norm_ax, pe=audio.positional_embeddings) * agate_msa
-            # Text cross-attention: no SP mask needed (text is replicated, uses local attention)
+            ax = ax + self.audio_attn1(norm_ax, pe=audio.positional_embeddings) * agate_msa
            ax = ax + self.audio_attn2(
                torch.nn.functional.rms_norm(ax, (ax.shape[-1],), eps=self.norm_eps),
                context=audio.context,
@@ -1596,91 +1197,28 @@ class BasicAVTransformerBlock(torch.nn.Module):
            if run_a2v:
                vx_scaled = vx_norm3 * (1 + scale_ca_video_hidden_states_a2v) + shift_ca_video_hidden_states_a2v
                ax_scaled = ax_norm3 * (1 + scale_ca_audio_hidden_states_a2v) + shift_ca_audio_hidden_states_a2v
-                # Audio-to-video cross-attention: Q from video (sharded), K/V from audio
-                # For SP, gather audio context from all ranks before cross-attention
-                if self.use_distributed_attention:
-                    # Gather audio context from all SP ranks
-                    ax_context = sequence_model_parallel_all_gather(ax_scaled, dim=1)
-                    # Video Q: video tokens divide evenly (adjusted upstream), so simple slicing works
-                    video_q_pe = None
-                    if video.cross_positional_embeddings is not None:
-                        sp_rank = get_sp_parallel_rank()
-                        local_seq_len = vx_scaled.shape[1]
-                        pe_tuple = video.cross_positional_embeddings
-                        start_idx = sp_rank * local_seq_len
-                        end_idx = start_idx + local_seq_len
-                        video_q_pe = tuple(pe[:, :, start_idx:end_idx, :] for pe in pe_tuple)
-                    # Audio K: gathered context may have padding, trim to original length
-                    audio_k_pe = audio.cross_positional_embeddings
-                    if audio_k_pe is not None:
-                        original_audio_len = audio_k_pe[0].shape[2]
-                        ax_context = ax_context[:, :original_audio_len, :]
-                    vx = vx + (
-                        self.audio_to_video_attn(
-                            vx_scaled,
-                            context=ax_context,
-                            pe=video_q_pe,
-                            k_pe=audio_k_pe,
-                        )
-                        * gate_out_a2v
-                    )
-                else:
-                    ax_context = ax_scaled
-                    video_q_pe = video.cross_positional_embeddings
-                    audio_k_pe = audio.cross_positional_embeddings
-                    vx = vx + (
-                        self.audio_to_video_attn(
-                            vx_scaled,
-                            context=ax_context,
-                            pe=video_q_pe,
-                            k_pe=audio_k_pe,
-                        )
-                        * gate_out_a2v
+                vx = vx + (
+                    self.audio_to_video_attn(
+                        vx_scaled,
+                        context=ax_scaled,
+                        pe=video.cross_positional_embeddings,
+                        k_pe=audio.cross_positional_embeddings,
                    )
+                    * gate_out_a2v
+                )

            if run_v2a:
                ax_scaled = ax_norm3 * (1 + scale_ca_audio_hidden_states_v2a) + shift_ca_audio_hidden_states_v2a
                vx_scaled = vx_norm3 * (1 + scale_ca_video_hidden_states_v2a) + shift_ca_video_hidden_states_v2a
-                # Video-to-audio cross-attention: Q from audio, K/V from video
-                # For SP, gather both modalities and run cross-attention on full sequences
-                # (audio is short, so no need to keep it sharded for cross-attention)
-                if self.use_distributed_attention:
-                    # Gather both modalities to full sequences
-                    ax_full = sequence_model_parallel_all_gather(ax_scaled, dim=1)
-                    vx_context = sequence_model_parallel_all_gather(vx_scaled, dim=1)
-                    # Use full positional embeddings directly
-                    audio_q_pe = audio.cross_positional_embeddings
-                    video_k_pe = video.cross_positional_embeddings
-                    # Trim gathered tensors to original lengths (may have padding from sharding)
-                    if audio_q_pe is not None:
-                        original_audio_len = audio_q_pe[0].shape[2]
-                        ax_full = ax_full[:, :original_audio_len, :]
-                    if video_k_pe is not None:
-                        original_video_len = video_k_pe[0].shape[2]
-                        vx_context = vx_context[:, :original_video_len, :]
-                    # Run cross-attention on full audio
-                    v2a_out_full = self.video_to_audio_attn(
-                        ax_full,
-                        context=vx_context,
-                        pe=audio_q_pe,
-                        k_pe=video_k_pe,
-                    )
-                    # Shard the output back to match ax's sharding
-                    v2a_out, _ = sequence_model_parallel_shard(v2a_out_full, dim=1)
-                    ax = ax + v2a_out * gate_out_v2a
-                else:
-                    vx_context = vx_scaled
-                    audio_q_pe = audio.cross_positional_embeddings
-                    video_k_pe = video.cross_positional_embeddings
-                    ax = ax + (
-                        self.video_to_audio_attn(
-                            ax_scaled,
-                            context=vx_context,
-                            pe=audio_q_pe,
-                            k_pe=video_k_pe,
-                        )
-                        * gate_out_v2a
+                ax = ax + (
+                    self.video_to_audio_attn(
+                        ax_scaled,
+                        context=vx_scaled,
+                        pe=audio.cross_positional_embeddings,
+                        k_pe=video.cross_positional_embeddings,
                    )
+                    * gate_out_v2a
+                )

        if run_vx:
            vshift_mlp, vscale_mlp, vgate_mlp = self.get_ada_values(
@@ -1751,8 +1289,6 @@ class LTXModel(torch.nn.Module):
        av_ca_timestep_scale_multiplier: int = 1,
        rope_type: LTXRopeType = LTXRopeType.INTERLEAVED,
        double_precision_rope: bool = False,
-        use_distributed_attention: bool = False,
-        prefix: str = "",
    ):
        super().__init__()
        self._enable_gradient_checkpointing = False
@@ -1804,8 +1340,6 @@ class LTXModel(torch.nn.Module):
            audio_attention_head_dim=audio_attention_head_dim if model_type.is_audio_enabled() else 0,
            audio_cross_attention_dim=audio_cross_attention_dim,
            norm_eps=norm_eps,
-            use_distributed_attention=use_distributed_attention,
-            prefix=prefix,
        )

    def _init_video(
@@ -1935,8 +1469,6 @@ class LTXModel(torch.nn.Module):
        audio_attention_head_dim: int,
        audio_cross_attention_dim: int,
        norm_eps: float,
-        use_distributed_attention: bool = False,
-        prefix: str = "",
    ) -> None:
        video_config = (
            TransformerConfig(
@@ -1958,7 +1490,6 @@ class LTXModel(torch.nn.Module):
            if self.model_type.is_audio_enabled()
            else None
        )
-        self.use_distributed_attention = use_distributed_attention
        self.transformer_blocks = torch.nn.ModuleList(
            [
                BasicAVTransformerBlock(
@@ -1967,8 +1498,6 @@ class LTXModel(torch.nn.Module):
                    audio=audio_config,
                    rope_type=self.rope_type,
                    norm_eps=norm_eps,
-                    use_distributed_attention=use_distributed_attention,
-                    prefix=prefix,
                )
                for idx in range(num_layers)
            ]
@@ -1978,16 +1507,9 @@ class LTXModel(torch.nn.Module):
        self,
        video: TransformerArgs | None,
        audio: TransformerArgs | None,
-        video_attention_mask: torch.Tensor | None = None,
-        audio_attention_mask: torch.Tensor | None = None,
    ) -> tuple[TransformerArgs | None, TransformerArgs | None]:
        for block in self.transformer_blocks:
-            video, audio = block(
-                video=video,
-                audio=audio,
-                video_attention_mask=video_attention_mask,
-                audio_attention_mask=audio_attention_mask,
-            )
+            video, audio = block(video=video, audio=audio)
        return video, audio

    def _process_output(
@@ -2011,17 +1533,7 @@ class LTXModel(torch.nn.Module):
        self,
        video: Modality | None,
        audio: Modality | None,
-        video_attention_mask: torch.Tensor | None = None,
-        audio_attention_mask: torch.Tensor | None = None,
    ) -> tuple[torch.Tensor | None, torch.Tensor | None]:
-        """Forward pass through the LTX model.
-
-        Args:
-            video: Video modality input
-            audio: Audio modality input
-            video_attention_mask: SP padding attention mask for video [B, padded_seq_len]
-            audio_attention_mask: SP padding attention mask for audio [B, padded_seq_len]
-        """
        if os.getenv("LTX2_PIPELINE_DEBUG_LOG", "0") == "1":
            _debug_block_log_line(
                "fastvideo:patchify_proj"
@@ -2039,12 +1551,7 @@ class LTXModel(torch.nn.Module):
        audio_args = self.audio_args_preprocessor.prepare(audio) if audio is not None else None
        _debug_transformer_args("fastvideo:prep_video", video_args)
        _debug_transformer_args("fastvideo:prep_audio", audio_args)
-        video_out, audio_out = self._process_transformer_blocks(
-            video_args,
-            audio_args,
-            video_attention_mask=video_attention_mask,
-            audio_attention_mask=audio_attention_mask,
-        )
+        video_out, audio_out = self._process_transformer_blocks(video_args, audio_args)

        vx = (
            self._process_output(
@@ -2081,24 +1588,6 @@ class LTX2Transformer3DModel(CachableDiT):

        arch = config.arch_config

-        # Get SP world size for distributed attention
-        sp_world_size = get_sp_world_size()
-        use_distributed_attention = sp_world_size > 1
-
-        # Validate that attention heads are divisible by SP world size
-        if sp_world_size > 1:
-            assert arch.num_attention_heads % sp_world_size == 0, (
-                f"The number of video attention heads ({arch.num_attention_heads}) "
-                f"must be divisible by the sequence parallel size ({sp_world_size})"
-            )
-            assert arch.audio_num_attention_heads % sp_world_size == 0, (
-                f"The number of audio attention heads ({arch.audio_num_attention_heads}) "
-                f"must be divisible by the sequence parallel size ({sp_world_size})"
-            )
-            logger.info(
-                f"LTX2 sequence parallelism enabled with SP world size {sp_world_size}"
-            )
-
        model_type = LTXModelType.AudioVideo
        self.model = LTXModel(
            model_type=model_type,
@@ -2123,8 +1612,6 @@ class LTX2Transformer3DModel(CachableDiT):
            audio_cross_attention_dim=arch.audio_cross_attention_dim,
            audio_positional_embedding_max_pos=arch.audio_positional_embedding_max_pos,
            av_ca_timestep_scale_multiplier=arch.av_ca_timestep_scale_multiplier,
-            use_distributed_attention=use_distributed_attention,
-            prefix=config.prefix,
        )

        self.patchifier = VideoLatentPatchifier(patch_size=arch.patch_size[1])
@@ -2140,7 +1627,6 @@ class LTX2Transformer3DModel(CachableDiT):
        self.hidden_size = arch.num_attention_heads * arch.attention_head_dim
        self.num_attention_heads = arch.num_attention_heads
        self.num_channels_latents = arch.num_channels_latents
-        self._logged_attention_mask = False

        if os.getenv("LTX2_DEBUG_DETAIL", "0") == "1":
            detail_path = os.getenv("LTX2_PIPELINE_DEBUG_DETAIL_PATH", "")
@@ -2216,12 +1702,10 @@ class LTX2Transformer3DModel(CachableDiT):
        if isinstance(encoder_hidden_states, list):
            encoder_hidden_states = encoder_hidden_states[0]

-        # Get SP parameters
-        sp_world_size = get_sp_world_size()
-        sp_rank = get_sp_parallel_rank()
-        batch_size = hidden_states.shape[0]
-
-        # Get fps for position computation
+        video_shape = VideoLatentShape.from_torch_shape(hidden_states.shape)
+        positions = self.patchifier.get_patch_grid_bounds(
+            video_shape, device=hidden_states.device
+        )
        fps = None
        try:
            forward_ctx = get_forward_context()
@@ -2233,60 +1717,17 @@ class LTX2Transformer3DModel(CachableDiT):
                fps_value = fps_value[0] if fps_value else None
            if fps_value is not None:
                fps = float(fps_value)
-
-        # Patchify video latents
-        video_shape = VideoLatentShape.from_torch_shape(hidden_states.shape)
-        latents = self.patchifier.patchify(hidden_states)
-
-        # Shard video latents and timestep across SP ranks
-        video_original_seq_len = latents.shape[1]
-        video_attention_mask = None
-        video_timestep = timestep
-        if sp_world_size > 1:
-            latents, video_original_seq_len = sequence_model_parallel_shard(latents, dim=1)
-            # Shard timestep along sequence dimension (timestep has shape [batch, seq_len])
-            video_timestep, _ = sequence_model_parallel_shard(timestep, dim=1)
-            # Create attention mask for padded tokens
-            current_seq_len = latents.shape[1]
-            padded_seq_len = current_seq_len * sp_world_size
-            if padded_seq_len > video_original_seq_len:
-                if not self._logged_attention_mask:
-                    logger.info(
-                        f"Video padding applied, original seq len: {video_original_seq_len}, "
-                        f"padded seq len: {padded_seq_len}"
-                    )
-                    self._logged_attention_mask = True
-                video_attention_mask = create_attention_mask_for_padding(
-                    seq_len=video_original_seq_len,
-                    padded_seq_len=padded_seq_len,
-                    batch_size=batch_size,
-                    device=hidden_states.device,
-                )
-
-        # Compute RoPE positions for the FULL sequence (before sharding)
-        # This is necessary because sequence sharding may not align to frame boundaries.
-        # The full RoPE will be passed to DistributedAttention which applies it after
-        # the all-to-all (when each rank has the full sequence but subset of heads).
-        positions = self.patchifier.get_patch_grid_bounds(
-            video_shape, device=hidden_states.device
-        )
        positions = _get_pixel_coords(
            positions,
            DEFAULT_LTX2_SCALE_FACTORS,
            fps=fps,
            causal_fix=True,
        ).to(hidden_states.dtype)
-
-        # Pad positions to match padded sequence length for SP cross-attention
-        if sp_world_size > 1 and padded_seq_len > video_original_seq_len:
-            padding_needed = padded_seq_len - video_original_seq_len
-            last_pos = positions[:, :, -1:, :].expand(-1, -1, padding_needed, -1)
-            positions = torch.cat([positions, last_pos], dim=2)
-
+        latents = self.patchifier.patchify(hidden_states)
        video_modality = Modality(
            enabled=True,
            latent=latents,
-            timesteps=video_timestep,
+            timesteps=timestep,
            positions=positions,
            context=encoder_hidden_states,
            context_mask=encoder_attention_mask,
@@ -2304,45 +1745,19 @@ class LTX2Transformer3DModel(CachableDiT):
                f"latent_head={video_head} "
                f"latent_checksum={video_checksum:.6f}"
            )
-
-        # Process audio modality if provided
        audio_modality = None
        audio_shape = None
-        audio_original_seq_len = 0
-        audio_attention_mask = None
-
        if audio_hidden_states is not None and audio_encoder_hidden_states is not None and audio_timestep is not None:
-            audio_shape = AudioLatentShape.from_torch_shape(audio_hidden_states.shape)
-            audio_latents = self.audio_patchifier.patchify(audio_hidden_states)
-
-            # Shard audio latents and timestep across SP ranks
-            audio_original_seq_len = audio_latents.shape[1]
-            sharded_audio_timestep = audio_timestep
-            if sp_world_size > 1:
-                audio_latents, audio_original_seq_len = sequence_model_parallel_shard(audio_latents, dim=1)
-                # Shard audio timestep along sequence dimension
-                sharded_audio_timestep, _ = sequence_model_parallel_shard(audio_timestep, dim=1)
-                # Create attention mask for padded tokens
-                audio_current_seq_len = audio_latents.shape[1]
-                audio_padded_seq_len = audio_current_seq_len * sp_world_size
-                if audio_padded_seq_len > audio_original_seq_len:
-                    audio_attention_mask = create_attention_mask_for_padding(
-                        seq_len=audio_original_seq_len,
-                        padded_seq_len=audio_padded_seq_len,
-                        batch_size=batch_size,
-                        device=audio_hidden_states.device,
-                    )
-
-            # Compute audio RoPE positions for the FULL sequence (before sharding)
-            # Same as video: full RoPE is applied after all-to-all in DistributedAttention
+            audio_shape = AudioLatentShape.from_torch_shape(
+                audio_hidden_states.shape)
            audio_positions = self.audio_patchifier.get_patch_grid_bounds(
-                audio_shape, device=audio_hidden_states.device
-            )
-
+                audio_shape, device=audio_hidden_states.device)
+            audio_latents = self.audio_patchifier.patchify(
+                audio_hidden_states)
            audio_modality = Modality(
                enabled=True,
                latent=audio_latents,
-                timesteps=sharded_audio_timestep,
+                timesteps=audio_timestep,
                positions=audio_positions,
                context=audio_encoder_hidden_states,
                context_mask=audio_encoder_attention_mask,
@@ -2360,16 +1775,10 @@ class LTX2Transformer3DModel(CachableDiT):
                    f"latent_head={audio_head} "
                    f"latent_checksum={audio_checksum:.6f}"
                )
-
-        # Run transformer with attention masks
        video_out, audio_out = self.model(
            video=video_modality,
            audio=audio_modality,
-            video_attention_mask=video_attention_mask,
-            audio_attention_mask=audio_attention_mask,
        )
-
-        # Denoised prediction
        if video_out is not None and video_modality is not None:
            video_out = _to_denoised(
                video_modality.latent,
@@ -2382,20 +1791,6 @@ class LTX2Transformer3DModel(CachableDiT):
                audio_out,
                audio_modality.timesteps,
            )
-
-        # Gather and unpad video output
-        if sp_world_size > 1 and video_out is not None:
-            video_out = sequence_model_parallel_all_gather_with_unpad(
-                video_out, video_original_seq_len, dim=1
-            )
-
-        # Gather and unpad audio output
-        if sp_world_size > 1 and audio_out is not None:
-            audio_out = sequence_model_parallel_all_gather_with_unpad(
-                audio_out, audio_original_seq_len, dim=1
-            )
-
-        # Unpatchify
        video_out = self.patchifier.unpatchify(
            video_out, output_shape=video_shape)
        if audio_out is None or audio_shape is None:
@@ -2403,6 +1798,3 @@ class LTX2Transformer3DModel(CachableDiT):
        audio_out = self.audio_patchifier.unpatchify(
            audio_out, output_shape=audio_shape)
        return video_out, audio_out
-
-# Entry point for model registry
-EntryClass = LTX2Transformer3DModel
@@ -9,6 +9,3 @@ __all__ = [
    "CausalMatrixGameTransformerBlock",
    "ActionModule",
 ]
-
-# Entry point for model registry
-EntryClass = [MatrixGameWanModel, CausalMatrixGameWanModel]
@@ -9,32 +9,20 @@ import torch.nn as nn
 from fastvideo.attention import DistributedAttention
 from fastvideo.configs.models.dits.matrixgame import MatrixGameWanVideoConfig
 from fastvideo.distributed.parallel_state import get_sp_world_size
-from fastvideo.layers.layernorm import (
-    FP32LayerNorm,
-    LayerNormScaleShift,
-    RMSNorm,
-    ScaleResidual,
-    ScaleResidualLayerNormScaleShift,
-)
+from fastvideo.layers.layernorm import (FP32LayerNorm, LayerNormScaleShift,
+                                        RMSNorm, ScaleResidual,
+                                        ScaleResidualLayerNormScaleShift)
 from fastvideo.layers.linear import ReplicatedLinear
 from fastvideo.layers.mlp import MLP
-from fastvideo.layers.rotary_embedding import (
-    _apply_rotary_emb,
-    get_rotary_pos_embed,
-)
-from fastvideo.layers.visual_embedding import (
-    PatchEmbed,
-    TimestepEmbedder,
-    ModulateProjection,
-)
+from fastvideo.layers.rotary_embedding import (_apply_rotary_emb,
+                                               get_rotary_pos_embed)
+from fastvideo.layers.visual_embedding import PatchEmbed, TimestepEmbedder, ModulateProjection
 from fastvideo.logger import init_logger
 from fastvideo.models.dits.base import BaseDiT
-from fastvideo.models.dits.wanvideo import (
-    WanSelfAttention,
-    WanI2VCrossAttention,
-    WanT2VCrossAttention,
-    WanImageEmbedding,
-)
+from fastvideo.models.dits.wanvideo import (WanSelfAttention,
+                                           WanI2VCrossAttention,
+                                           WanT2VCrossAttention,
+                                           WanImageEmbedding)
 from fastvideo.platforms import AttentionBackendEnum, current_platform

 # Import ActionModule
@@ -53,12 +41,11 @@ class MatrixGameTimeImageEmbedding(nn.Module):
        super().__init__()

        self.time_embedder = TimestepEmbedder(
-            dim, frequency_embedding_size=time_freq_dim, act_layer="silu"
-        )
-        self.time_modulation = ModulateProjection(
-            dim, factor=6, act_layer="silu"
-        )
-
+            dim, frequency_embedding_size=time_freq_dim, act_layer="silu")
+        self.time_modulation = ModulateProjection(dim,
+                                                  factor=6,
+                                                  act_layer="silu")
+        
        self.image_embedder = None
        if image_embed_dim is not None:
            self.image_embedder = WanImageEmbedding(image_embed_dim, dim)
@@ -66,7 +53,7 @@ class MatrixGameTimeImageEmbedding(nn.Module):
    def forward(
        self,
        timestep: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,  # Kept for interface compatibility
+        encoder_hidden_states: torch.Tensor, # Kept for interface compatibility
        encoder_hidden_states_image: torch.Tensor | None = None,
        timestep_seq_len: int | None = None,
    ):
@@ -74,25 +61,17 @@ class MatrixGameTimeImageEmbedding(nn.Module):
        timestep_proj = self.time_modulation(temb)

        # MatrixGame does not use text embeddings, so we ignore encoder_hidden_states
-
+        
        if encoder_hidden_states_image is not None:
            assert self.image_embedder is not None
            encoder_hidden_states_image = self.image_embedder(
-                encoder_hidden_states_image
-            )
+                encoder_hidden_states_image)

-        encoder_hidden_states = torch.zeros(
-            (timestep.shape[0], 0, temb.shape[-1]),
-            device=temb.device,
-            dtype=temb.dtype,
-        )
+        encoder_hidden_states = torch.zeros((timestep.shape[0], 0, temb.shape[-1]),
+                                            device=temb.device,
+                                            dtype=temb.dtype)

-        return (
-            temb,
-            timestep_proj,
-            encoder_hidden_states,
-            encoder_hidden_states_image,
-        )
+        return temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image


 class MatrixGameCrossAttention(WanSelfAttention):
@@ -108,7 +87,7 @@ class MatrixGameCrossAttention(WanSelfAttention):

        # compute query, key, value
        q = self.norm_q(self.to_q(x)[0]).view(b, -1, n, d)
-
+        
        if crossattn_cache is not None:
            if not crossattn_cache["is_init"]:
                crossattn_cache["is_init"] = True
@@ -122,7 +101,7 @@ class MatrixGameCrossAttention(WanSelfAttention):
        else:
            k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
            v = self.to_v(context)[0].view(b, -1, n, d)
-
+        
        # compute attention
        x = self.attn(q, k, v)

@@ -133,20 +112,19 @@ class MatrixGameCrossAttention(WanSelfAttention):


 class MatrixGameTransformerBlock(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        ffn_dim: int,
-        num_heads: int,
-        qk_norm: str = "rms_norm_across_heads",
-        cross_attn_norm: bool = False,
-        eps: float = 1e-6,
-        added_kv_proj_dim: int | None = None,
-        supported_attention_backends: tuple[AttentionBackendEnum, ...]
-        | None = None,
-        prefix: str = "",
-        action_config: dict | None = None,
-    ):
+    
+    def __init__(self,
+                 dim: int,
+                 ffn_dim: int,
+                 num_heads: int,
+                 qk_norm: str = "rms_norm_across_heads",
+                 cross_attn_norm: bool = False,
+                 eps: float = 1e-6,
+                 added_kv_proj_dim: int | None = None,
+                 supported_attention_backends: tuple[AttentionBackendEnum, ...]
+                 | None = None,
+                 prefix: str = "",
+                 action_config: dict | None = None):
        super().__init__()
        action_config = action_config or {}

@@ -162,8 +140,7 @@ class MatrixGameTransformerBlock(nn.Module):
            head_size=dim // num_heads,
            causal=False,
            supported_attention_backends=supported_attention_backends,
-            prefix=f"{prefix}.attn1",
-        )
+            prefix=f"{prefix}.attn1")
        self.hidden_dim = dim
        self.num_attention_heads = num_heads
        dim_head = dim // num_heads
@@ -184,28 +161,28 @@ class MatrixGameTransformerBlock(nn.Module):
            eps=eps,
            elementwise_affine=True,
            dtype=torch.float32,
-            compute_dtype=torch.float32,
-        )
+            compute_dtype=torch.float32)

        # 2. Cross-attention
        if added_kv_proj_dim is not None:
            # I2V
-            self.attn2 = WanI2VCrossAttention(
-                dim, num_heads, qk_norm=qk_norm, eps=eps
-            )
+            self.attn2 = WanI2VCrossAttention(dim,
+                                              num_heads,
+                                              qk_norm=qk_norm,
+                                              eps=eps)
        else:
            # T2V
-            self.attn2 = WanT2VCrossAttention(
-                dim, num_heads, qk_norm=qk_norm, eps=eps
-            )
+            self.attn2 = WanT2VCrossAttention(dim,
+                                              num_heads,
+                                              qk_norm=qk_norm,
+                                              eps=eps)
        self.cross_attn_residual_norm = ScaleResidualLayerNormScaleShift(
            dim,
            norm_type="layer",
            eps=eps,
            elementwise_affine=False,
            dtype=torch.float32,
-            compute_dtype=torch.float32,
-        )
+            compute_dtype=torch.float32)

        # 2.1. Action Module Integration
        self.use_action_module = len(action_config) > 0
@@ -227,7 +204,7 @@ class MatrixGameTransformerBlock(nn.Module):
        temb: torch.Tensor,
        freqs_cis: tuple[torch.Tensor, torch.Tensor],
        # Action Module specific args
-        grid_sizes: torch.Tensor,
+        grid_sizes: torch.Tensor | None = None,
        mouse_cond: torch.Tensor | None = None,
        keyboard_cond: torch.Tensor | None = None,
    ) -> torch.Tensor:
@@ -237,16 +214,9 @@ class MatrixGameTransformerBlock(nn.Module):

        if temb.dim() == 4:
            # temb: batch_size, seq_len, 6, inner_dim (wan2.2 ti2v)
-            (
-                shift_msa,
-                scale_msa,
-                gate_msa,
-                c_shift_msa,
-                c_scale_msa,
-                c_gate_msa,
-            ) = (self.scale_shift_table.unsqueeze(0) + temb.float()).chunk(
-                6, dim=2
-            )
+            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
+                self.scale_shift_table.unsqueeze(0) + temb.float()
+            ).chunk(6, dim=2)
            # batch_size, seq_len, 1, inner_dim
            shift_msa = shift_msa.squeeze(2)
            scale_msa = scale_msa.squeeze(2)
@@ -257,20 +227,13 @@ class MatrixGameTransformerBlock(nn.Module):
        else:
            # temb: batch_size, 6, inner_dim (wan2.1/wan2.2 14B)
            e = self.scale_shift_table + temb.float()
-            (
-                shift_msa,
-                scale_msa,
-                gate_msa,
-                c_shift_msa,
-                c_scale_msa,
-                c_gate_msa,
-            ) = e.chunk(6, dim=1)
+            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(
+                6, dim=1)
        assert shift_msa.dtype == torch.float32

        # 1. Self-attention
-        norm_hidden_states = (
-            self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa
-        ).to(orig_dtype)
+        norm_hidden_states = (self.norm1(hidden_states.float()) *
+                              (1 + scale_msa) + shift_msa).to(orig_dtype)
        query, _ = self.to_q(norm_hidden_states)
        key, _ = self.to_k(norm_hidden_states)
        value, _ = self.to_v(norm_hidden_states)
@@ -286,10 +249,9 @@ class MatrixGameTransformerBlock(nn.Module):

        # Apply rotary embeddings
        cos, sin = freqs_cis
-        query, key = (
-            _apply_rotary_emb(query, cos, sin, is_neox_style=False),
-            _apply_rotary_emb(key, cos, sin, is_neox_style=False),
-        )
+        query, key = _apply_rotary_emb(query, cos, sin,
+                                       is_neox_style=False), _apply_rotary_emb(
+                                           key, cos, sin, is_neox_style=False)

        attn_output, _ = self.attn1(query, key, value)
        attn_output = attn_output.flatten(2)
@@ -298,24 +260,18 @@ class MatrixGameTransformerBlock(nn.Module):

        null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
        norm_hidden_states, hidden_states = self.self_attn_residual_norm(
-            hidden_states, attn_output, gate_msa, null_shift, null_scale
-        )
-        norm_hidden_states, hidden_states = (
-            norm_hidden_states.to(orig_dtype),
-            hidden_states.to(orig_dtype),
-        )
+            hidden_states, attn_output, gate_msa, null_shift, null_scale)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)

        # 2. Cross-attention
-        attn_output = self.attn2(
-            norm_hidden_states, context=encoder_hidden_states, context_lens=None
-        )
+        attn_output = self.attn2(norm_hidden_states,
+                                 context=encoder_hidden_states,
+                                 context_lens=None)
        norm_hidden_states, hidden_states = self.cross_attn_residual_norm(
-            hidden_states, attn_output, 1, c_shift_msa, c_scale_msa
-        )
-        norm_hidden_states, hidden_states = (
-            norm_hidden_states.to(orig_dtype),
-            hidden_states.to(orig_dtype),
-        )
+            hidden_states, attn_output, 1, c_shift_msa, c_scale_msa)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)

        # ================= Action Module =================
        if self.action_model is not None:
@@ -324,12 +280,9 @@ class MatrixGameTransformerBlock(nn.Module):
                # ActionModule implementation takes hidden_states directly
                hidden_states = self.action_model(
                    hidden_states,
-                    int(grid_sizes[0]),
-                    int(grid_sizes[1]),
-                    int(grid_sizes[2]),
-                    mouse_cond,
-                    keyboard_cond,
-                    num_frame_per_block=int(grid_sizes[0]),
+                    grid_sizes[0], grid_sizes[1], grid_sizes[2],
+                    mouse_cond, keyboard_cond,
+                    num_frame_per_block=grid_sizes[0],
                )
        # =================================================

@@ -340,7 +293,6 @@ class MatrixGameTransformerBlock(nn.Module):

        return hidden_states

-
 _DEFAULT_MATRIXGAME_CONFIG = MatrixGameWanVideoConfig()


@@ -350,23 +302,15 @@ class MatrixGameWanModel(BaseDiT):

    _fsdp_shard_conditions = _DEFAULT_MATRIXGAME_CONFIG._fsdp_shard_conditions
    _compile_conditions = _DEFAULT_MATRIXGAME_CONFIG._compile_conditions
-    _supported_attention_backends = (
-        _DEFAULT_MATRIXGAME_CONFIG._supported_attention_backends
-    )
+    _supported_attention_backends = _DEFAULT_MATRIXGAME_CONFIG._supported_attention_backends
    param_names_mapping = _DEFAULT_MATRIXGAME_CONFIG.param_names_mapping
-    reverse_param_names_mapping = (
-        _DEFAULT_MATRIXGAME_CONFIG.reverse_param_names_mapping
-    )
-    lora_param_names_mapping = (
-        _DEFAULT_MATRIXGAME_CONFIG.lora_param_names_mapping
-    )
+    reverse_param_names_mapping = _DEFAULT_MATRIXGAME_CONFIG.reverse_param_names_mapping
+    lora_param_names_mapping = _DEFAULT_MATRIXGAME_CONFIG.lora_param_names_mapping

-    def __init__(
-        self,
-        config: MatrixGameWanVideoConfig,
-        hf_config: dict[str, Any],
-        **kwargs,
-    ) -> None:
+    def __init__(self,
+                 config: MatrixGameWanVideoConfig,
+                 hf_config: dict[str, Any],
+                 **kwargs) -> None:
        super().__init__(config=config, hf_config=hf_config)

        inner_dim = config.num_attention_heads * config.attention_head_dim
@@ -376,12 +320,10 @@ class MatrixGameWanModel(BaseDiT):
        self.patch_size = config.patch_size

        # 1. Patch & position embedding
-        self.patch_embedding = PatchEmbed(
-            in_chans=config.in_channels,
-            embed_dim=inner_dim,
-            patch_size=config.patch_size,
-            flatten=False,
-        )
+        self.patch_embedding = PatchEmbed(in_chans=config.in_channels,
+                                          embed_dim=inner_dim,
+                                          patch_size=config.patch_size,
+                                          flatten=False)

        # 2. Condition embeddings
        self.condition_embedder = MatrixGameTimeImageEmbedding(
@@ -391,73 +333,57 @@ class MatrixGameWanModel(BaseDiT):
        )

        # 2.1. Get action config
-        self.action_config = getattr(config, "action_config", {})
+        self.action_config = getattr(config, 'action_config', {})

        # 3. Transformer blocks
-        self.blocks = nn.ModuleList(
-            [
-                MatrixGameTransformerBlock(
-                    inner_dim,
-                    config.ffn_dim,
-                    config.num_attention_heads,
-                    config.qk_norm,
-                    config.cross_attn_norm,
-                    config.eps,
-                    config.added_kv_proj_dim,
-                    self._supported_attention_backends,
-                    prefix=f"{getattr(config, 'prefix', 'Wan')}.blocks.{i}",
-                    action_config=self.action_config,
-                )
-                for i in range(config.num_layers)
-            ]
-        )
+        self.blocks = nn.ModuleList([
+            MatrixGameTransformerBlock(
+                inner_dim,
+                config.ffn_dim,
+                config.num_attention_heads,
+                config.qk_norm,
+                config.cross_attn_norm,
+                config.eps,
+                config.added_kv_proj_dim,
+                self._supported_attention_backends,
+                prefix=f"{getattr(config, 'prefix', 'Wan')}.blocks.{i}",
+                action_config=self.action_config)
+            for i in range(config.num_layers)
+        ])

        # 4. Output norm & projection
-        self.norm_out = LayerNormScaleShift(
-            inner_dim,
-            norm_type="layer",
-            eps=config.eps,
-            elementwise_affine=False,
-            dtype=torch.float32,
-            compute_dtype=torch.float32,
-        )
+        self.norm_out = LayerNormScaleShift(inner_dim,
+                                            norm_type="layer",
+                                            eps=config.eps,
+                                            elementwise_affine=False,
+                                            dtype=torch.float32,
+                                            compute_dtype=torch.float32)
        self.proj_out = nn.Linear(
-            inner_dim, config.out_channels * math.prod(config.patch_size)
-        )
+            inner_dim, config.out_channels * math.prod(config.patch_size))
        self.scale_shift_table = nn.Parameter(
-            torch.randn(1, 2, inner_dim) / inner_dim**0.5
-        )
-
+            torch.randn(1, 2, inner_dim) / inner_dim**0.5)
+ 
        self.gradient_checkpointing = False

-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor | list[torch.Tensor],
-        timestep: torch.LongTensor,
-        encoder_hidden_states_image: torch.Tensor
-        | list[torch.Tensor]
-        | None = None,
-        # Action inputs
-        mouse_cond: torch.Tensor | None = None,
-        keyboard_cond: torch.Tensor | None = None,
-        **kwargs,
-    ) -> torch.Tensor:
-        if encoder_hidden_states is not None and not isinstance(
-            encoder_hidden_states, torch.Tensor
-        ):
+    def forward(self,
+                hidden_states: torch.Tensor,
+                encoder_hidden_states: torch.Tensor | list[torch.Tensor],
+                timestep: torch.LongTensor,
+                encoder_hidden_states_image: torch.Tensor
+                | list[torch.Tensor] | None = None,
+                # Action inputs
+                mouse_cond: torch.Tensor | None = None,
+                keyboard_cond: torch.Tensor | None = None,
+                **kwargs) -> torch.Tensor:
+        if encoder_hidden_states is not None and not isinstance(encoder_hidden_states, torch.Tensor):
            encoder_hidden_states = encoder_hidden_states[0]
-        if (
-            isinstance(encoder_hidden_states_image, list)
-            and len(encoder_hidden_states_image) > 0
-        ):
+        if isinstance(encoder_hidden_states_image,
+                      list) and len(encoder_hidden_states_image) > 0:
            encoder_hidden_states_image = encoder_hidden_states_image[0]
        else:
            encoder_hidden_states_image = None

-        batch_size, num_channels, num_frames, height, width = (
-            hidden_states.shape
-        )
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
        p_t, p_h, p_w = self.patch_size
        post_patch_num_frames = num_frames // p_t
        post_patch_height = height // p_h
@@ -467,25 +393,18 @@ class MatrixGameWanModel(BaseDiT):
        d = self.hidden_size // self.num_attention_heads
        rope_dim_list = [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]
        freqs_cos, freqs_sin = get_rotary_pos_embed(
-            (
-                post_patch_num_frames * get_sp_world_size(),
-                post_patch_height,
-                post_patch_width,
-            ),
+            (post_patch_num_frames * get_sp_world_size(), post_patch_height,
+             post_patch_width),
            self.hidden_size,
            self.num_attention_heads,
            rope_dim_list,
            dtype=torch.float32 if current_platform.is_mps() else torch.float64,
            rope_theta=10000,
-            do_sp_sharding=True,
-        )
+            do_sp_sharding=True)
        freqs_cos = freqs_cos.to(hidden_states.device)
        freqs_sin = freqs_sin.to(hidden_states.device)
-        freqs_cis = (
-            (freqs_cos.float(), freqs_sin.float())
-            if freqs_cos is not None
-            else None
-        )
+        freqs_cis = (freqs_cos.float(),
+                     freqs_sin.float()) if freqs_cos is not None else None

        hidden_states = self.patch_embedding(hidden_states)
        hidden_states = hidden_states.flatten(2).transpose(1, 2)
@@ -493,14 +412,8 @@ class MatrixGameWanModel(BaseDiT):
        if timestep.dim() == 2:
            timestep = timestep.flatten()

-        (
-            temb,
-            timestep_proj,
-            encoder_hidden_states,
-            encoder_hidden_states_image,
-        ) = self.condition_embedder(
-            timestep, encoder_hidden_states, encoder_hidden_states_image
-        )
+        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
+            timestep, encoder_hidden_states, encoder_hidden_states_image)
        timestep_proj = timestep_proj.unflatten(1, (6, -1))

        if encoder_hidden_states is not None:
@@ -516,30 +429,25 @@ class MatrixGameWanModel(BaseDiT):
        if encoder_hidden_states_image is not None:
            if encoder_hidden_states is not None:
                encoder_hidden_states = torch.concat(
-                    [encoder_hidden_states_image, encoder_hidden_states], dim=1
-                )
+                    [encoder_hidden_states_image, encoder_hidden_states], dim=1)
            else:
                encoder_hidden_states = encoder_hidden_states_image

        # This is [F, H, W] for the ActionModule
-        grid_sizes = torch.tensor(
-            [post_patch_num_frames, post_patch_height, post_patch_width],
-            device=hidden_states.device,
-        )
+        grid_sizes = torch.tensor([
+            post_patch_num_frames, post_patch_height, post_patch_width
+        ],
+                                 device=hidden_states.device)

        # Blocks
        for block in self.blocks:
            if torch.is_grad_enabled() and self.gradient_checkpointing:
                hidden_states = self._gradient_checkpointing_func(
-                    block,
-                    hidden_states,
-                    encoder_hidden_states,
-                    timestep_proj,
+                    block, hidden_states, encoder_hidden_states, timestep_proj,
                    freqs_cis,
                    grid_sizes=grid_sizes,
                    mouse_cond=mouse_cond,
-                    keyboard_cond=keyboard_cond,
-                )
+                    keyboard_cond=keyboard_cond)
            else:
                hidden_states = block(
                    hidden_states,
@@ -548,27 +456,19 @@ class MatrixGameWanModel(BaseDiT):
                    freqs_cis,
                    grid_sizes=grid_sizes,
                    mouse_cond=mouse_cond,
-                    keyboard_cond=keyboard_cond,
-                )
+                    keyboard_cond=keyboard_cond)

        # Output
-        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(
-            2, dim=1
-        )
+        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2,
+                                                                          dim=1)
        hidden_states = self.norm_out(hidden_states, shift, scale)
        hidden_states = self.proj_out(hidden_states)

-        hidden_states = hidden_states.reshape(
-            batch_size,
-            post_patch_num_frames,
-            post_patch_height,
-            post_patch_width,
-            p_t,
-            p_h,
-            p_w,
-            -1,
-        )
+        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
+                                              post_patch_height,
+                                              post_patch_width, p_t, p_h, p_w,
+                                              -1)
        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)

-        return output
+        return output
@@ -685,6 +685,3 @@ class StepVideoModel(BaseDiT):

        output = rearrange(output, '(b f) c h w -> b c f h w', f=frame)
        return output
-
-# Entry point for model registry
-EntryClass = StepVideoModel
@@ -0,0 +1,12 @@
+from .model import WanGameActionTransformer3DModel
+from .causal_model import (CausalWanGameTransformer3DModel,
+                           CausalWanTransformer3DModel)
+from .hyworld_action_module import WanGameActionTimeImageEmbedding, WanGameActionSelfAttention
+
+__all__ = [
+    "WanGameActionTransformer3DModel",
+    "CausalWanTransformer3DModel",
+    "CausalWanGameTransformer3DModel",
+    "WanGameActionTimeImageEmbedding",
+    "WanGameActionSelfAttention",
+]
@@ -0,0 +1,98 @@
+# SPDX-License-Identifier: Apache-2.0
+from typing import Any
+
+import torch
+
+from fastvideo.configs.models.dits.wangamevideo import WanGameVideoConfig
+from fastvideo.models.dits.matrixgame.causal_model import CausalMatrixGameWanModel
+
+_DEFAULT_WANGAME_CAUSAL_CONFIG = WanGameVideoConfig()
+
+
+class CausalWanTransformer3DModel(CausalMatrixGameWanModel):
+    supports_action_input = False
+
+    _fsdp_shard_conditions = _DEFAULT_WANGAME_CAUSAL_CONFIG._fsdp_shard_conditions
+    _compile_conditions = _DEFAULT_WANGAME_CAUSAL_CONFIG._compile_conditions
+    _supported_attention_backends = _DEFAULT_WANGAME_CAUSAL_CONFIG._supported_attention_backends
+    param_names_mapping = _DEFAULT_WANGAME_CAUSAL_CONFIG.param_names_mapping
+    reverse_param_names_mapping = _DEFAULT_WANGAME_CAUSAL_CONFIG.reverse_param_names_mapping
+    lora_param_names_mapping = _DEFAULT_WANGAME_CAUSAL_CONFIG.lora_param_names_mapping
+
+    def __init__(self,
+                 config: WanGameVideoConfig,
+                 hf_config: dict[str, Any],
+                 **kwargs) -> None:
+        super().__init__(config=config, hf_config=hf_config, **kwargs)
+
+    def _normalize_action_inputs(
+        self,
+        mouse_cond: torch.Tensor | None,
+        keyboard_cond: torch.Tensor | None,
+    ) -> tuple[torch.Tensor | None, torch.Tensor | None]:
+        # drop
+        if len(getattr(self, "action_config", {})) == 0:
+            return None, None
+        return mouse_cond, keyboard_cond
+
+    def _forward_inference(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | list[torch.Tensor],
+        timestep: torch.LongTensor,
+        encoder_hidden_states_image: torch.Tensor | list[torch.Tensor] | None = None,
+        mouse_cond: torch.Tensor | None = None,
+        keyboard_cond: torch.Tensor | None = None,
+        kv_cache: dict | None = None,
+        kv_cache_mouse: dict | None = None,
+        kv_cache_keyboard: dict | None = None,
+        crossattn_cache: dict | None = None,
+        current_start: int = 0,
+        cache_start: int = 0,
+        start_frame: int = 0,
+        **kwargs,
+    ) -> torch.Tensor:
+        mouse_cond, keyboard_cond = self._normalize_action_inputs(
+            mouse_cond, keyboard_cond)
+        return super()._forward_inference(
+            hidden_states=hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+            timestep=timestep,
+            encoder_hidden_states_image=encoder_hidden_states_image,
+            mouse_cond=mouse_cond,
+            keyboard_cond=keyboard_cond,
+            kv_cache=kv_cache,
+            kv_cache_mouse=kv_cache_mouse,
+            kv_cache_keyboard=kv_cache_keyboard,
+            crossattn_cache=crossattn_cache,
+            current_start=current_start,
+            cache_start=cache_start,
+            start_frame=start_frame,
+            **kwargs,
+        )
+
+    def _forward_train(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | list[torch.Tensor],
+        timestep: torch.LongTensor,
+        encoder_hidden_states_image: torch.Tensor | list[torch.Tensor] | None = None,
+        mouse_cond: torch.Tensor | None = None,
+        keyboard_cond: torch.Tensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        mouse_cond, keyboard_cond = self._normalize_action_inputs(
+            mouse_cond, keyboard_cond)
+        return super()._forward_train(
+            hidden_states=hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+            timestep=timestep,
+            encoder_hidden_states_image=encoder_hidden_states_image,
+            mouse_cond=mouse_cond,
+            keyboard_cond=keyboard_cond,
+            **kwargs,
+        )
+
+
+class CausalWanGameTransformer3DModel(CausalWanTransformer3DModel):
+    pass
@@ -0,0 +1,289 @@
+import math
+
+import torch
+import torch.nn as nn
+
+from fastvideo.layers.visual_embedding import TimestepEmbedder, ModulateProjection, timestep_embedding
+from fastvideo.platforms import AttentionBackendEnum
+from fastvideo.attention import DistributedAttention
+from fastvideo.forward_context import set_forward_context
+from fastvideo.models.dits.wanvideo import WanImageEmbedding
+
+from fastvideo.models.dits.hyworld.camera_rope import prope_qkv
+from fastvideo.layers.rotary_embedding import _apply_rotary_emb
+from fastvideo.layers.mlp import MLP
+
+class WanGameActionTimeImageEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        time_freq_dim: int,
+        image_embed_dim: int | None = None,
+    ):
+        super().__init__()
+
+        self.time_freq_dim = time_freq_dim
+        self.time_embedder = TimestepEmbedder(
+            dim, frequency_embedding_size=time_freq_dim, act_layer="silu")
+        self.time_modulation = ModulateProjection(dim,
+                                                  factor=6,
+                                                  act_layer="silu")
+        
+        self.image_embedder = None
+        if image_embed_dim is not None:
+            self.image_embedder = WanImageEmbedding(image_embed_dim, dim)
+
+        self.action_embedder = MLP(
+            time_freq_dim,
+            dim,
+            dim,
+            bias=True,
+            act_type="silu"
+        )
+        # Initialize fc_in with kaiming_uniform (same as nn.Linear default)
+        nn.init.kaiming_uniform_(self.action_embedder.fc_in.weight, a=math.sqrt(5))
+        # Initialize fc_out with zeros for residual-like behavior
+        nn.init.zeros_(self.action_embedder.fc_out.weight)
+        if self.action_embedder.fc_out.bias is not None:
+            nn.init.zeros_(self.action_embedder.fc_out.bias)
+
+    def forward(
+        self,
+        timestep: torch.Tensor,
+        action: torch.Tensor,
+        encoder_hidden_states: torch.Tensor, # Kept for interface compatibility
+        encoder_hidden_states_image: torch.Tensor | None = None,
+        timestep_seq_len: int | None = None,
+    ):
+        """
+        Args:
+            timestep: [B] diffusion timesteps (one per batch sample)
+            action: [B, T] action labels (one per frame per batch sample)
+        
+        Returns:
+            temb: [B*T, dim] combined timestep + action embedding
+            timestep_proj: [B*T, 6*dim] modulation projection
+        """
+        # timestep: [B] -> temb: [B, dim]
+        temb = self.time_embedder(timestep, timestep_seq_len)
+        
+        # Handle action embedding for batch > 1
+        # action shape: [B, T] where B=batch_size, T=num_frames
+        batch_size = action.shape[0]
+        num_frames = action.shape[1]
+        
+        # Compute action embeddings: [B, T] -> [B*T] -> [B*T, dim]
+        action_flat = action.flatten()  # [B*T]
+        action_emb = timestep_embedding(action_flat, self.time_freq_dim)
+        action_embedder_dtype = next(iter(self.action_embedder.parameters())).dtype
+        if (
+            action_emb.dtype != action_embedder_dtype
+            and action_embedder_dtype != torch.int8
+        ):
+            action_emb = action_emb.to(action_embedder_dtype)
+        action_emb = self.action_embedder(action_emb).type_as(temb)  # [B*T, dim]
+        
+        # Expand temb to match action_emb: [B, dim] -> [B, T, dim] -> [B*T, dim]
+        # Each batch's temb is repeated for all its frames
+        temb_expanded = temb.unsqueeze(1).expand(-1, num_frames, -1)  # [B, T, dim]
+        temb_expanded = temb_expanded.reshape(batch_size * num_frames, -1)  # [B*T, dim]
+        
+        # Add action embedding to expanded temb
+        temb = temb_expanded + action_emb  # [B*T, dim]
+
+        timestep_proj = self.time_modulation(temb)  # [B*T, 6*dim]
+
+        # MatrixGame does not use text embeddings, so we ignore encoder_hidden_states
+        
+        if encoder_hidden_states_image is not None:
+            assert self.image_embedder is not None
+            encoder_hidden_states_image = self.image_embedder(
+                encoder_hidden_states_image)
+
+        encoder_hidden_states = torch.zeros((batch_size, 0, temb.shape[-1]),
+                                            device=temb.device,
+                                            dtype=temb.dtype)
+
+        return temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image
+
+class WanGameActionSelfAttention(nn.Module):
+    """
+    Self-attention module with support for:
+    - Standard RoPE-based attention
+    - Camera PRoPE-based attention (when viewmats and Ks are provided)
+    - KV caching for autoregressive generation
+    """
+
+    def __init__(self,
+                 dim: int,
+                 num_heads: int,
+                 local_attn_size: int = -1,
+                 sink_size: int = 0,
+                 qk_norm=True,
+                 eps=1e-6) -> None:
+        assert dim % num_heads == 0
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.local_attn_size = local_attn_size
+        self.sink_size = sink_size
+        self.qk_norm = qk_norm
+        self.eps = eps
+        self.max_attention_size = 32760 if local_attn_size == -1 else local_attn_size * 1560
+
+        # Scaled dot product attention (using DistributedAttention for SP support)
+        self.attn = DistributedAttention(
+            num_heads=num_heads,
+            head_size=self.head_dim,
+            softmax_scale=None,
+            causal=False,
+            supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
+                                          AttentionBackendEnum.TORCH_SDPA))
+
+    def forward(self,
+                q: torch.Tensor,
+                k: torch.Tensor,
+                v: torch.Tensor,
+                freqs_cis: tuple[torch.Tensor, torch.Tensor],
+                kv_cache: dict | None = None,
+                current_start: int = 0,
+                cache_start: int | None = None,
+                viewmats: torch.Tensor | None = None,
+                Ks: torch.Tensor | None = None,
+                is_cache: bool = False,
+                attention_mask: torch.Tensor | None = None):
+        """
+        Forward pass with camera PRoPE attention combining standard RoPE and projective positional encoding.
+        
+        Args:
+            q, k, v: Query, key, value tensors [B, L, num_heads, head_dim]
+            freqs_cis: RoPE frequency cos/sin tensors
+            kv_cache: KV cache dict (may have None values for training)
+            current_start: Current position for KV cache
+            cache_start: Cache start position
+            viewmats: Camera view matrices for PRoPE [B, cameras, 4, 4]
+            Ks: Camera intrinsics for PRoPE [B, cameras, 3, 3]
+            is_cache: Whether to store to KV cache (for inference)
+            attention_mask: Attention mask [B, L] (1 = attend, 0 = mask)
+        """
+        if cache_start is None:
+            cache_start = current_start
+
+        # Apply RoPE manually
+        cos, sin = freqs_cis
+        query_rope = _apply_rotary_emb(q, cos, sin, is_neox_style=False).type_as(v)
+        key_rope = _apply_rotary_emb(k, cos, sin, is_neox_style=False).type_as(v)
+        value_rope = v
+
+        # # DEBUG: Check camera matrices
+        # if self.training and torch.distributed.get_rank() == 0:
+        #     vm_info = f"viewmats={viewmats.shape if viewmats is not None else None}"
+        #     ks_info = f"Ks={Ks.shape if Ks is not None else None}"
+        #     vm_nonzero = (viewmats != 0).sum().item() if viewmats is not None else 0
+        #     ks_nonzero = (Ks != 0).sum().item() if Ks is not None else 0
+        #     print(f"[DEBUG] PRoPE input: {vm_info} nonzero={vm_nonzero}, {ks_info} nonzero={ks_nonzero}", flush=True)
+        
+        # Get PRoPE transformed q, k, v
+        query_prope, key_prope, value_prope, apply_fn_o = prope_qkv(
+            q.transpose(1, 2),  # [B, num_heads, L, head_dim]
+            k.transpose(1, 2),
+            v.transpose(1, 2),
+            viewmats=viewmats,
+            Ks=Ks,
+            patches_x=40,  # hardcoded for now
+            patches_y=22,
+        )
+        # PRoPE returns [B, num_heads, L, head_dim], convert to [B, L, num_heads, head_dim]
+        query_prope = query_prope.transpose(1, 2)
+        key_prope = key_prope.transpose(1, 2)
+        value_prope = value_prope.transpose(1, 2)
+        
+        # # DEBUG: Check prope_qkv output
+        # if self.training and torch.distributed.get_rank() == 0:
+        #     q_nz = (query_prope != 0).sum().item()
+        #     k_nz = (key_prope != 0).sum().item()
+        #     v_nz = (value_prope != 0).sum().item()
+        #     print(f"[DEBUG] prope_qkv output: q_nonzero={q_nz}, k_nonzero={k_nz}, v_nonzero={v_nz}", flush=True)
+
+        # KV cache handling
+        if kv_cache is not None:
+            cache_key = kv_cache.get("k", None)
+            cache_value = kv_cache.get("v", None)
+
+            if cache_value is not None and not is_cache:
+                cache_key_rope, cache_key_prope = cache_key.chunk(2, dim=-1)
+                cache_value_rope, cache_value_prope = cache_value.chunk(2, dim=-1)
+
+                key_rope = torch.cat([cache_key_rope, key_rope], dim=1)
+                value_rope = torch.cat([cache_value_rope, value_rope], dim=1)
+                key_prope = torch.cat([cache_key_prope, key_prope], dim=1)
+                value_prope = torch.cat([cache_value_prope, value_prope], dim=1)
+
+            if is_cache:
+                # Store to cache (update input dict directly)
+                kv_cache["k"] = torch.cat([key_rope, key_prope], dim=-1)
+                kv_cache["v"] = torch.cat([value_rope, value_prope], dim=-1)
+
+        # Concatenate rope and prope paths (matching original)
+        query_all = torch.cat([query_rope, query_prope], dim=0)
+        key_all = torch.cat([key_rope, key_prope], dim=0)
+        value_all = torch.cat([value_rope, value_prope], dim=0)
+
+        # Check if Q and KV have different sequence lengths (KV cache mode)
+        # In this case, use LocalAttention (supports different Q/KV lengths)
+        if query_all.shape[1] != key_all.shape[1]:
+            # KV cache mode: Q has new tokens only, KV has cached + new tokens
+            # Use LocalAttention which supports different Q/KV lengths
+            # LocalAttention will use the appropriate backend (SageAttn, FlashAttn, etc.)
+            if not hasattr(self, '_kv_cache_attn'):
+                from fastvideo.attention import LocalAttention
+                self._kv_cache_attn = LocalAttention(
+                    num_heads=self.num_heads,
+                    head_size=self.head_dim,
+                    causal=False,
+                    supported_attention_backends=(AttentionBackendEnum.SAGE_ATTN,
+                                                  AttentionBackendEnum.FLASH_ATTN,
+                                                  AttentionBackendEnum.TORCH_SDPA)
+                )
+            hidden_states_all = self._kv_cache_attn(query_all, key_all, value_all)
+        else:
+            # Same sequence length: use DistributedAttention (supports SP)
+            # Create default attention mask if not provided
+            # NOTE: query_all has shape [2*B, L, ...] (rope+prope concatenated), so mask needs 2*B
+            if attention_mask is None:
+                batch_size, seq_len = q.shape[0], q.shape[1]
+                attention_mask = torch.ones(batch_size * 2, seq_len, device=q.device, dtype=q.dtype)
+        
+            from fastvideo.attention.backends.sdpa import SDPAMetadataBuilder
+            attn_metadata_builder = SDPAMetadataBuilder
+            if q.dtype != torch.float32:
+                try:
+                    from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
+                    attn_metadata_builder = FlashAttnMetadataBuilder
+                except ImportError:
+                    pass  # fall back to SDPA
+            attn_metadata = attn_metadata_builder().build(
+                current_timestep=0,
+                attn_mask=attention_mask,
+            )
+            with set_forward_context(current_timestep=0, attn_metadata=attn_metadata):
+                hidden_states_all, _ = self.attn(query_all, key_all, value_all, attention_mask=attention_mask)
+
+        hidden_states_rope, hidden_states_prope = hidden_states_all.chunk(2, dim=0)
+        
+        # # DEBUG: Check attention output and apply_fn_o
+        # if self.training and torch.distributed.get_rank() == 0:
+        #     attn_all_nz = (hidden_states_all != 0).sum().item()
+        #     rope_nz = (hidden_states_rope != 0).sum().item()
+        #     prope_before = (hidden_states_prope != 0).sum().item()
+        #     print(f"[DEBUG] attn output: all_nonzero={attn_all_nz}, rope_nonzero={rope_nz}, prope_before_apply={prope_before}", flush=True)
+        
+        hidden_states_prope = apply_fn_o(hidden_states_prope.transpose(1, 2)).transpose(1, 2)
+        
+        # # DEBUG: Check after apply_fn_o
+        # if self.training and torch.distributed.get_rank() == 0:
+        #     prope_after = (hidden_states_prope != 0).sum().item()
+        #     print(f"[DEBUG] prope_after_apply_fn_o={prope_after}", flush=True)
+
+        return hidden_states_rope, hidden_states_prope
@@ -0,0 +1,427 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import math
+from typing import Any
+
+import torch
+import torch.nn as nn
+
+from fastvideo.configs.models.dits.wangamevideo import WanGameVideoConfig
+from fastvideo.distributed.parallel_state import get_sp_world_size
+from fastvideo.layers.layernorm import (FP32LayerNorm, LayerNormScaleShift,
+                                        RMSNorm, ScaleResidual,
+                                        ScaleResidualLayerNormScaleShift)
+from fastvideo.layers.linear import ReplicatedLinear
+from fastvideo.layers.mlp import MLP
+from fastvideo.layers.rotary_embedding import (_apply_rotary_emb,
+                                               get_rotary_pos_embed)
+from fastvideo.layers.visual_embedding import PatchEmbed
+from fastvideo.logger import init_logger
+from fastvideo.models.dits.base import BaseDiT
+from fastvideo.models.dits.wanvideo import WanI2VCrossAttention
+from fastvideo.platforms import AttentionBackendEnum, current_platform
+
+# Import ActionModule
+from fastvideo.models.dits.wangame.hyworld_action_module import WanGameActionTimeImageEmbedding, WanGameActionSelfAttention
+
+logger = init_logger(__name__)
+
+
+class WanGameCrossAttention(WanI2VCrossAttention):
+    def forward(self, x, context, context_lens=None):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L1, C]
+            context(Tensor): Shape [B, L2, C]
+            context_lens(Tensor): Shape [B]
+        """
+        context_img = context
+        b, n, d = x.size(0), self.num_heads, self.head_dim
+
+        # compute query, key, value
+        q = self.norm_q(self.to_q(x)[0]).view(b, -1, n, d)
+        k_img = self.norm_added_k(self.add_k_proj(context_img)[0]).view(
+            b, -1, n, d)
+        v_img = self.add_v_proj(context_img)[0].view(b, -1, n, d)
+        img_x = self.attn(q, k_img, v_img)
+
+        # output
+        x = img_x.flatten(2)
+        x, _ = self.to_out(x)
+        return x
+
+class WanGameActionTransformerBlock(nn.Module):
+    """
+    Transformer block for WAN Action model with support for:
+    - Self-attention with RoPE and camera PRoPE
+    - Cross-attention with text/image context
+    - Feed-forward network with AdaLN modulation
+    """
+
+    def __init__(self,
+                 dim: int,
+                 ffn_dim: int,
+                 num_heads: int,
+                 local_attn_size: int = -1,
+                 sink_size: int = 0,
+                 qk_norm: str = "rms_norm_across_heads",
+                 cross_attn_norm: bool = False,
+                 eps: float = 1e-6,
+                 added_kv_proj_dim: int | None = None,
+                 supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
+                 prefix: str = ""):
+        super().__init__()
+
+        # 1. Self-attention
+        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
+        self.to_q = ReplicatedLinear(dim, dim, bias=True)
+        self.to_k = ReplicatedLinear(dim, dim, bias=True)
+        self.to_v = ReplicatedLinear(dim, dim, bias=True)
+        self.to_out = ReplicatedLinear(dim, dim, bias=True)
+        
+        self.attn1 = WanGameActionSelfAttention(
+            dim,
+            num_heads,
+            local_attn_size=local_attn_size,
+            sink_size=sink_size,
+            qk_norm=qk_norm,
+            eps=eps)
+        
+        self.hidden_dim = dim
+        self.num_attention_heads = num_heads
+        self.local_attn_size = local_attn_size
+        dim_head = dim // num_heads
+
+        if qk_norm == "rms_norm":
+            self.norm_q = RMSNorm(dim_head, eps=eps)
+            self.norm_k = RMSNorm(dim_head, eps=eps)
+        elif qk_norm == "rms_norm_across_heads":
+            self.norm_q = RMSNorm(dim, eps=eps)
+            self.norm_k = RMSNorm(dim, eps=eps)
+        else:
+            raise ValueError(f"QK Norm type {qk_norm} not supported")
+
+        assert cross_attn_norm is True
+        self.self_attn_residual_norm = ScaleResidualLayerNormScaleShift(
+            dim,
+            norm_type="layer",
+            eps=eps,
+            elementwise_affine=True,
+            compute_dtype=torch.float32)
+
+        # 2. Cross-attention (I2V only for now)
+        self.attn2 = WanGameCrossAttention(dim,
+                                          num_heads,
+                                          qk_norm=qk_norm,
+                                          eps=eps)
+        # norm3 for FFN input 
+        self.norm3 = LayerNormScaleShift(dim, norm_type="layer", eps=eps,
+                                         elementwise_affine=False)
+
+        # 3. Feed-forward
+        self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
+        self.mlp_residual = ScaleResidual()
+
+        self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
+
+        # PRoPE output projection (initialized via add_discrete_action_parameters on the model)
+        self.to_out_prope = ReplicatedLinear(dim, dim, bias=True)
+        nn.init.zeros_(self.to_out_prope.weight)
+        if self.to_out_prope.bias is not None:
+            nn.init.zeros_(self.to_out_prope.bias)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        freqs_cis: tuple[torch.Tensor, torch.Tensor],
+        kv_cache: dict | None = None,
+        crossattn_cache: dict | None = None,
+        current_start: int = 0,
+        cache_start: int | None = None,
+        viewmats: torch.Tensor | None = None,
+        Ks: torch.Tensor | None = None,
+        is_cache: bool = False,
+    ) -> torch.Tensor:
+        if hidden_states.dim() == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        num_frames = temb.shape[1]
+        frame_seqlen = hidden_states.shape[1] // num_frames
+        bs, seq_length, _ = hidden_states.shape
+        orig_dtype = hidden_states.dtype
+
+        # Cast temb to float32 for scale/shift computation
+        e = self.scale_shift_table + temb.float()
+        assert e.shape == (bs, num_frames, 6, self.hidden_dim)
+        shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(6, dim=2)
+
+        # 1. Self-attention
+        norm_hidden_states = (self.norm1(hidden_states.float()).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) *
+            (1 + scale_msa) + shift_msa).to(orig_dtype).flatten(1, 2)
+        
+        query, _ = self.to_q(norm_hidden_states)
+        key, _ = self.to_k(norm_hidden_states)
+        value, _ = self.to_v(norm_hidden_states)
+
+        if self.norm_q is not None:
+            query = self.norm_q.forward_native(query)
+        if self.norm_k is not None:
+            key = self.norm_k.forward_native(key)
+
+        query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
+        key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
+        value = value.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
+
+        # Self-attention with camera PRoPE
+        attn_output_rope, attn_output_prope = self.attn1(
+            query, key, value, freqs_cis,
+            kv_cache, current_start, cache_start, viewmats, Ks,
+            is_cache=is_cache
+        )
+        # Combine rope and prope outputs
+        attn_output_rope = attn_output_rope.flatten(2)
+        attn_output_rope, _ = self.to_out(attn_output_rope)
+        attn_output_prope = attn_output_prope.flatten(2)
+        
+        # # DEBUG: Check if prope input is zero
+        # if self.training and torch.distributed.get_rank() == 0:
+        #     prope_nonzero = (attn_output_prope != 0).sum().item()
+        #     prope_total = attn_output_prope.numel()
+        #     if prope_nonzero == 0:
+        #         print(f"[DEBUG] to_out_prope INPUT is ALL ZEROS! shape={attn_output_prope.shape}", flush=True)
+        
+        attn_output_prope, _ = self.to_out_prope(attn_output_prope)
+        attn_output = attn_output_rope.squeeze(1) + attn_output_prope.squeeze(1)
+
+        # Self-attention residual + norm in float32
+        null_shift = null_scale = torch.zeros(1, device=hidden_states.device, dtype=torch.float32)
+        norm_hidden_states, hidden_states = self.self_attn_residual_norm(
+            hidden_states.float(), attn_output.float(), gate_msa, null_shift, null_scale)
+        hidden_states = hidden_states.type_as(attn_output)
+        norm_hidden_states = norm_hidden_states.type_as(attn_output)
+
+        # 2. Cross-attention
+        attn_output = self.attn2(norm_hidden_states.to(orig_dtype),
+                                 context=encoder_hidden_states,
+                                 context_lens=None)
+        # Cross-attention residual in bfloat16
+        hidden_states = hidden_states + attn_output
+        
+        # norm3 for FFN input in float32
+        norm_hidden_states = self.norm3(
+            hidden_states.float(), c_shift_msa, c_scale_msa
+        ).type_as(hidden_states)
+
+        # 3. Feed-forward
+        ff_output = self.ffn(norm_hidden_states.to(orig_dtype))
+        hidden_states = self.mlp_residual(hidden_states.float(), ff_output.float(), c_gate_msa)
+        hidden_states = hidden_states.to(orig_dtype)  # Cast back to original dtype
+
+        return hidden_states
+
+class WanGameActionTransformer3DModel(BaseDiT):
+    """
+    WAN Action Transformer 3D Model for video generation with action conditioning.
+
+    Extends the base WAN video model with:
+    - Action embedding support for controllable generation
+    - camera PRoPE attention for 3D-aware generation
+    - KV caching for autoregressive inference
+    """
+    _uses_discrete_action = True  # Uses single action tensor [B, T] instead of keyboard/mouse KV caches
+    _kv_cache_head_dim_multiplier = 2  # PRoPE stores cat([rope, prope], dim=-1) in KV cache
+
+    _fsdp_shard_conditions = WanGameVideoConfig()._fsdp_shard_conditions
+    _compile_conditions = WanGameVideoConfig()._compile_conditions
+    _supported_attention_backends = WanGameVideoConfig()._supported_attention_backends
+    param_names_mapping = WanGameVideoConfig().param_names_mapping
+    reverse_param_names_mapping = WanGameVideoConfig().reverse_param_names_mapping
+    lora_param_names_mapping = WanGameVideoConfig().lora_param_names_mapping
+
+    def __init__(self, config: WanGameVideoConfig, hf_config: dict[str, Any]) -> None:
+        super().__init__(config=config, hf_config=hf_config)
+
+        inner_dim = config.num_attention_heads * config.attention_head_dim
+        self.hidden_size = config.hidden_size
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_dim = config.attention_head_dim
+        self.in_channels = config.in_channels
+        self.out_channels = config.out_channels
+        self.num_channels_latents = config.num_channels_latents
+        self.patch_size = config.patch_size
+        self.local_attn_size = config.local_attn_size
+        self.inner_dim = inner_dim
+
+        # 1. Patch & position embedding
+        self.patch_embedding = PatchEmbed(in_chans=config.in_channels,
+                                          embed_dim=inner_dim,
+                                          patch_size=config.patch_size,
+                                          flatten=False)
+
+        # 2. Condition embeddings (with action support)
+        self.condition_embedder = WanGameActionTimeImageEmbedding(
+            dim=inner_dim,
+            time_freq_dim=config.freq_dim,
+            image_embed_dim=config.image_dim,
+        )
+
+        # 3. Transformer blocks
+        self.blocks = nn.ModuleList([
+            WanGameActionTransformerBlock(
+                inner_dim,
+                config.ffn_dim,
+                config.num_attention_heads,
+                config.local_attn_size,
+                config.sink_size,
+                config.qk_norm,
+                config.cross_attn_norm,
+                config.eps,
+                config.added_kv_proj_dim,
+                supported_attention_backends=self._supported_attention_backends,
+                prefix=f"{config.prefix}.blocks.{i}")
+            for i in range(config.num_layers)
+        ])
+
+        # 4. Output norm & projection
+        self.norm_out = LayerNormScaleShift(inner_dim,
+                                            norm_type="layer",
+                                            eps=config.eps,
+                                            elementwise_affine=False,
+                                            dtype=torch.float32)
+        self.proj_out = nn.Linear(
+            inner_dim, config.out_channels * math.prod(config.patch_size))
+        self.scale_shift_table = nn.Parameter(torch.randn(1, 2, inner_dim) / inner_dim**0.5)
+
+        self.gradient_checkpointing = False
+
+        # Causal-specific
+        self.num_frame_per_block = config.arch_config.num_frames_per_block
+        assert self.num_frame_per_block <= 3
+
+        self.__post_init__()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | list[torch.Tensor],
+        timestep: torch.LongTensor,
+        encoder_hidden_states_image: torch.Tensor | list[torch.Tensor],
+        guidance=None,
+        action: torch.Tensor | None = None,
+        viewmats: torch.Tensor | None = None,
+        Ks: torch.Tensor | None = None,
+        kv_cache: list[dict] | None = None,
+        crossattn_cache: list[dict] | None = None,
+        current_start: int = 0,
+        cache_start: int = 0,
+        start_frame: int = 0,
+        is_cache: bool = False,
+        **kwargs
+    ) -> torch.Tensor:
+        """
+        Forward pass for both training and inference with KV caching.
+        
+        Args:
+            hidden_states: Video latents [B, C, T, H, W]
+            encoder_hidden_states: Text embeddings [B, L, D]
+            timestep: Timestep tensor
+            encoder_hidden_states_image: Optional image embeddings
+            action: Action tensor [B, T] for per-frame conditioning
+            viewmats: Camera view matrices for PRoPE [B, T, 4, 4]
+            Ks: Camera intrinsics for PRoPE [B, T, 3, 3]
+            kv_cache: KV cache for autoregressive inference (list of dicts per layer)
+            crossattn_cache: Cross-attention cache for inference
+            current_start: Current position for KV cache
+            cache_start: Cache start position
+            start_frame: RoPE offset for new frames in autoregressive mode
+            is_cache: If True, populate KV cache and return early (cache-only mode)
+        """
+        orig_dtype = hidden_states.dtype
+        # if not isinstance(encoder_hidden_states, torch.Tensor):
+        #     encoder_hidden_states = encoder_hidden_states[0]
+        if isinstance(encoder_hidden_states_image, list) and len(encoder_hidden_states_image) > 0:
+            encoder_hidden_states_image = encoder_hidden_states_image[0]
+        # else:
+        #     encoder_hidden_states_image = None
+
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        p_t, p_h, p_w = self.patch_size
+        post_patch_num_frames = num_frames // p_t
+        post_patch_height = height // p_h
+        post_patch_width = width // p_w
+
+        # Get rotary embeddings
+        d = self.hidden_size // self.num_attention_heads
+        rope_dim_list = [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]
+        freqs_cos, freqs_sin = get_rotary_pos_embed(
+            (post_patch_num_frames * get_sp_world_size(), post_patch_height, post_patch_width),
+            self.hidden_size,
+            self.num_attention_heads,
+            rope_dim_list,
+            dtype=torch.float32 if current_platform.is_mps() else torch.float64,
+            rope_theta=10000,
+            start_frame=start_frame
+        )
+        freqs_cos = freqs_cos.to(hidden_states.device)
+        freqs_sin = freqs_sin.to(hidden_states.device)
+        freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None
+
+        hidden_states = self.patch_embedding(hidden_states)
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+
+        if timestep.dim() == 2:
+            # condition_embedder expects [B] timestep, not [B*T].
+            # All frames share the same timestep, so take first column.
+            timestep = timestep[:, 0]
+
+        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
+            timestep, action, encoder_hidden_states, encoder_hidden_states_image=encoder_hidden_states_image)
+        
+        # condition_embedder returns:
+        # - temb: [B*T, dim] where T = post_patch_num_frames
+        # - timestep_proj: [B*T, 6*dim]
+        # Reshape to [B, T, 6, dim] for transformer blocks
+        timestep_proj = timestep_proj.unflatten(1, (6, self.hidden_size))  # [B*T, 6, dim]
+        timestep_proj = timestep_proj.view(batch_size, post_patch_num_frames, 6, self.hidden_size)  # [B, T, 6, dim]
+
+        encoder_hidden_states = encoder_hidden_states_image
+
+        # Transformer blocks
+        for block_idx, block in enumerate(self.blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                hidden_states = self._gradient_checkpointing_func(
+                    block, hidden_states, encoder_hidden_states, timestep_proj, freqs_cis,
+                    kv_cache[block_idx] if kv_cache else None,
+                    crossattn_cache[block_idx] if crossattn_cache else None,
+                    current_start, cache_start,
+                    viewmats, Ks, is_cache)
+            else:
+                hidden_states = block(
+                    hidden_states, encoder_hidden_states, timestep_proj, freqs_cis,
+                    kv_cache[block_idx] if kv_cache else None,
+                    crossattn_cache[block_idx] if crossattn_cache else None,
+                    current_start, cache_start,
+                    viewmats, Ks, is_cache)
+
+        # If cache-only mode, return early
+        if is_cache:
+            return kv_cache
+
+        # Output norm, projection & unpatchify
+        # temb is [B*T, dim], reshape to [B, T, 1, dim]
+        temb = temb.view(batch_size, post_patch_num_frames, -1).unsqueeze(2)  # [B, T, 1, dim]
+        
+        shift, scale = (self.scale_shift_table.unsqueeze(1) + temb).chunk(2, dim=2)
+        hidden_states = self.norm_out(hidden_states, shift, scale)
+        hidden_states = self.proj_out(hidden_states)
+
+        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
+                                              post_patch_height,
+                                              post_patch_width, p_t, p_h, p_w,
+                                              -1)
+        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
+
+        return output
@@ -871,6 +871,4 @@ class WanTransformer3DModel(CachableDiT):
            return hidden_states + self.previous_residual_even
        else:
            return hidden_states + self.previous_residual_odd
-
-# Entry point for model registry
-EntryClass = WanTransformer3DModel
+        
@@ -631,6 +631,3 @@ class CLIPVisionModel(ImageEncoder):
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params
-
-# Entry point for model registry
-EntryClass = [CLIPTextModel, CLIPVisionModel]
@@ -561,6 +561,3 @@ def _norm_and_concat_padded_batch(
    mask_flattened = mask.reshape(b, t, 1).expand(-1, -1, d * l)
    normed = normed.masked_fill(~mask_flattened, 0.0)
    return normed
-
-# Entry point for model registry
-EntryClass = LTX2GemmaTextEncoderModel
@@ -429,6 +429,3 @@ class LlamaModel(TextEncoder):
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params
-
-# Entry point for model registry
-EntryClass = LlamaModel
@@ -385,6 +385,3 @@ class Qwen2_5_VLTextModel(TextEncoder):
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params
-
-# Entry point for model registry
-EntryClass = Qwen2_5_VLTextModel
@@ -351,5 +351,3 @@ class Reason1TextEncoder(TextEncoder):
            tensor.std(dim=-1, keepdim=True) + 1e-8
        )

-# Entry point for model registry
-EntryClass = Reason1TextEncoder
@@ -417,6 +417,3 @@ class SiglipVisionModel(ImageEncoder):
            loaded_params.add(name)
            
        return loaded_params
-
-# Entry point for model registry
-EntryClass = SiglipVisionModel
@@ -583,6 +583,3 @@ class STEP1TextEncoder(torch.nn.Module):
                                      self.device) if with_mask else None)
            y_mask = txt_tokens.attention_mask
        return y.transpose(0, 1), y_mask
-
-# Entry point for model registry
-EntryClass = STEP1TextEncoder
@@ -719,6 +719,3 @@ class UMT5EncoderModel(TextEncoder):
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params
-
-# Entry point for model registry
-EntryClass = [UMT5EncoderModel, T5EncoderModel]
@@ -79,7 +79,6 @@ class ComponentLoader(ABC):
            "scheduler": (SchedulerLoader, "diffusers"),
            "transformer": (TransformerLoader, "diffusers"),
            "transformer_2": (TransformerLoader, "diffusers"),
-            "transformer_3": (TransformerLoader, "diffusers"),
            "vae": (VAELoader, "diffusers"),
            "audio_vae": (AudioDecoderLoader, "diffusers"),
            "audio_decoder": (AudioDecoderLoader, "diffusers"),
@@ -91,8 +90,6 @@ class ComponentLoader(ABC):
            "image_processor": (ImageProcessorLoader, "transformers"),
            "feature_extractor": (ImageProcessorLoader, "transformers"),
            "image_encoder": (ImageEncoderLoader, "transformers"),
-            "upsampler": (UpsamplerLoader, "diffusers"),
-            "upsampler_2": (UpsamplerLoader, "diffusers"),
        }

        if module_type in module_loaders:
@@ -561,8 +558,7 @@ class VAELoader(ComponentLoader):
    def load(self, model_path: str, fastvideo_args: FastVideoArgs):
        """Load the VAE based on the model path, and inference args."""
        config = get_diffusers_config(model=model_path)
-        class_name = config.pop("_class_name")
-        config.pop("_name_or_path", None)
+        class_name = config.get("_class_name")
        assert class_name is not None, (
            "Model config does not contain a _class_name attribute. Only diffusers format is supported."
        )
@@ -735,7 +731,6 @@ class TransformerLoader(ComponentLoader):
        config = get_diffusers_config(model=model_path)
        hf_config = deepcopy(config)
        cls_name = config.pop("_class_name")
-        config.pop("_name_or_path", None)
        if cls_name is None:
            raise ValueError(
                "Model config does not contain a _class_name attribute. "
@@ -750,7 +745,7 @@ class TransformerLoader(ComponentLoader):
        fastvideo_args.model_paths["transformer"] = model_path

        # Config from Diffusers supersedes fastvideo's model config
-        dit_config = deepcopy(fastvideo_args.pipeline_config.dit_config)
+        dit_config = fastvideo_args.pipeline_config.dit_config
        dit_config.update_model_arch(config)

        model_cls, _ = ModelRegistry.resolve_model_cls(cls_name)
@@ -882,50 +877,6 @@ class SchedulerLoader(ComponentLoader):
        return scheduler


-class UpsamplerLoader(ComponentLoader):
-    """Loader for upsamplers."""
-
-    def load(self, model_path: str, fastvideo_args: FastVideoArgs):
-        """Load the upsampler based on the model path, and inference args."""
-        config_dict = get_diffusers_config(model=model_path)
-        class_name = config_dict.pop("_class_name", None)
-
-        if class_name is None:
-            raise ValueError(
-                "Model config does not contain a _class_name attribute. "
-                "Only diffusers format is supported."
-            )
-
-        try:
-            upsampler_cfg = deepcopy(fastvideo_args.pipeline_config.upsampler_config[0])
-            upsampler_cfg.update_model_config(config_dict)
-        except Exception as e:
-            upsampler_cfg = deepcopy(fastvideo_args.pipeline_config.upsampler_config[1])
-            upsampler_cfg.update_model_config(config_dict)
-
-        model_cls, _ = ModelRegistry.resolve_model_cls(class_name)
-        model = model_cls(upsampler_cfg)
-
-        target_device = get_local_torch_device()
-        model = model.to(target_device, dtype=PRECISION_TO_TYPE[fastvideo_args.pipeline_config.upsampler_precision])
-
-        # Find all safetensors files
-        safetensors_list = glob.glob(
-            os.path.join(str(model_path), "*.safetensors"))
-        if not safetensors_list:
-            raise ValueError(f"No safetensors files found in {model_path}")
-        
-        if len(safetensors_list) == 1:
-            loaded = safetensors_load_file(safetensors_list[0])
-        else:
-            loaded = {}
-            for sf_file in safetensors_list:
-                loaded.update(safetensors_load_file(sf_file))
-        
-        model.load_state_dict(loaded, strict=True)
-
-        return model.eval()
-
 class GenericComponentLoader(ComponentLoader):
    """Generic loader for components that don't have a specific loader."""

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # Adapted from vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/model_executor/models/registry.py

-import ast
 import importlib
 import os
 import pickle
@@ -45,6 +44,8 @@ _IMAGE_TO_VIDEO_DIT_MODELS = {
    "CausalWanTransformer3DModel": ("dits", "causal_wanvideo", "CausalWanTransformer3DModel"),
    "MatrixGameWanModel": ("dits", "matrixgame", "MatrixGameWanModel"),
    "CausalMatrixGameWanModel": ("dits", "matrixgame", "CausalMatrixGameWanModel"),
+    "WanGameActionTransformer3DModel": ("dits", "wangame", "WanGameActionTransformer3DModel"),
+    "CausalWanGameTransformer3DModel": ("dits", "wangame", "CausalWanGameTransformer3DModel"),
 }

 _TEXT_ENCODER_MODELS = {
@@ -99,12 +100,7 @@ _SCHEDULERS = {
    ("schedulers", "scheduling_rcm", "RCMScheduler"),
 }

-_UPSAMPLERS = {
-    "SRTo720pUpsampler": ("upsamplers", "hunyuan15", "SRTo720pUpsampler"),
-    "SRTo1080pUpsampler": ("upsamplers", "hunyuan15", "SRTo1080pUpsampler"),
-}
-
-_LEGACY_FAST_VIDEO_MODELS = {
+_FAST_VIDEO_MODELS = {
    **_TEXT_TO_VIDEO_DIT_MODELS,
    **_IMAGE_TO_VIDEO_DIT_MODELS,
    **_TEXT_ENCODER_MODELS,
@@ -112,102 +108,8 @@ _LEGACY_FAST_VIDEO_MODELS = {
    **_VAE_MODELS,
    **_AUDIO_MODELS,
    **_SCHEDULERS,
-    **_UPSAMPLERS,
 }

-MODELS_PATH = os.path.dirname(__file__)
-
-
-@lru_cache(maxsize=None)
-def _discover_and_register_models() -> dict[str, tuple[str, str, str]]:
-    discovered_models: dict[str, tuple[str, str, str]] = {}
-    for root, dirs, files in os.walk(MODELS_PATH):
-        dirs[:] = [
-            d for d in dirs
-            if not d.startswith(".") and d != "__pycache__"
-        ]
-
-        for filename in files:
-            if not filename.endswith(".py"):
-                continue
-
-            filepath = os.path.join(root, filename)
-            try:
-                with open(filepath, "r", encoding="utf-8") as f:
-                    source = f.read()
-                tree = ast.parse(source, filename=filename)
-
-                entry_class_node = None
-                first_class_def = None
-
-                for node in ast.walk(tree):
-                    if isinstance(node, ast.Assign):
-                        for target in node.targets:
-                            if isinstance(target, ast.Name) and target.id == "EntryClass":
-                                entry_class_node = node
-                                break
-                    if first_class_def is None and isinstance(node, ast.ClassDef):
-                        first_class_def = node
-
-                if not entry_class_node or not first_class_def:
-                    continue
-
-                model_cls_name_list: list[str] = []
-                value_node = entry_class_node.value
-
-                if isinstance(value_node, ast.Name):
-                    model_cls_name_list.append(value_node.id)
-                elif isinstance(value_node, (ast.List, ast.Tuple)):
-                    for elt in value_node.elts:
-                        if isinstance(elt, ast.Constant) and isinstance(
-                                elt.value, str):
-                            model_cls_name_list.append(elt.value)
-                        elif isinstance(elt, ast.Name):
-                            model_cls_name_list.append(elt.id)
-
-                if not model_cls_name_list:
-                    continue
-
-                rel_dir = os.path.relpath(root, MODELS_PATH)
-                if rel_dir == ".":
-                    continue
-
-                rel_parts = rel_dir.split(os.sep)
-                component_name = rel_parts[0]
-                sub_parts = rel_parts[1:]
-
-                if filename == "__init__.py":
-                    mod_relname = ".".join(sub_parts)
-                else:
-                    mod_base = filename[:-3]
-                    mod_relname = ".".join(sub_parts +
-                                           [mod_base]) if sub_parts else mod_base
-
-                for model_cls_str in model_cls_name_list:
-                    if model_cls_str in discovered_models:
-                        logger.warning(
-                            "Duplicate architecture found: %s. Overwriting.",
-                            model_cls_str)
-                    discovered_models[model_cls_str] = (
-                        component_name,
-                        mod_relname,
-                        model_cls_str,
-                    )
-
-            except Exception as e:
-                logger.warning("Could not parse %s to find models: %s",
-                               filepath, e)
-
-    return discovered_models
-
-
-_DISCOVERED_MODELS = _discover_and_register_models()
-_FAST_VIDEO_MODELS = dict(_DISCOVERED_MODELS)
-for model_arch, spec in _LEGACY_FAST_VIDEO_MODELS.items():
-    if model_arch in _FAST_VIDEO_MODELS:
-        continue
-    _FAST_VIDEO_MODELS[model_arch] = spec
-
 _SUBPROCESS_COMMAND = [sys.executable, "-m", "fastvideo.models.dits.registry"]

 _T = TypeVar("_T")
@@ -439,8 +341,7 @@ class _ModelRegistry:
 ModelRegistry = _ModelRegistry({
    model_arch:
    _LazyRegisteredModel(
-        module_name=(f"fastvideo.models.{component_name}.{mod_relname}"
-                     if mod_relname else f"fastvideo.models.{component_name}"),
+        module_name=f"fastvideo.models.{component_name}.{mod_relname}",
        component_name=component_name,
        class_name=cls_name,
    )
@@ -685,6 +685,3 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin,

    def __len__(self) -> int:
        return self.config.num_train_timesteps
-
-# Entry point for model registry
-EntryClass = FlowMatchEulerDiscreteScheduler
@@ -853,6 +853,3 @@ class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):

    def __len__(self):
        return self.config.num_train_timesteps
-
-# Entry point for model registry
-EntryClass = FlowUniPCMultistepScheduler
@@ -321,6 +321,3 @@ class RCMScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):

    def __len__(self) -> int:
        return self.config.num_train_timesteps
-
-# Entry point for model registry
-EntryClass = RCMScheduler
@@ -160,6 +160,4 @@ class SelfForcingFlowMatchScheduler(BaseScheduler, ConfigMixin, SchedulerMixin):

    def set_shift(self, shift: float) -> None:
        self.shift = shift
-
-# Entry point for model registry
-EntryClass = SelfForcingFlowMatchScheduler
+    
@@ -1094,7 +1094,4 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):
        return noisy_samples

    def __len__(self):
-        return self.config.num_train_timesteps
-
-# Entry point for model registry
-EntryClass = UniPCMultistepScheduler
+        return self.config.num_train_timesteps
@@ -1,170 +0,0 @@
-# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/main/LICENSE
-#
-# Unless and only to the extent required by applicable law, the Tencent Hunyuan works and any
-# output and results therefrom are provided "AS IS" without any express or implied warranties of
-# any kind including any warranties of title, merchantability, noninfringement, course of dealing,
-# usage of trade, or fitness for a particular purpose. You are solely responsible for determining the
-# appropriateness of using, reproducing, modifying, performing, displaying or distributing any of
-# the Tencent Hunyuan works or outputs and assume any and all risks associated with your or a
-# third party's use or distribution of any of the Tencent Hunyuan works or outputs and your exercise
-# of rights and permissions under this agreement.
-# See the License for the specific language governing permissions and limitations under the License.
-
-from collections.abc import Sequence
-from dataclasses import dataclass
-from enum import Enum
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange
-from torch import Tensor
-
-from fastvideo.models.vaes.hunyuan15vae import (
-    HunyuanVideo15CausalConv3d,
-    HunyuanVideo15RMS_norm,
-)
-
-from fastvideo.layers.activation import get_act_fn
-from fastvideo.configs.models.upsamplers import SRTo720pUpsamplerConfig, SRTo1080pUpsamplerConfig
-
-class HunyuanVideo15ResnetBlock(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int | None = None,
-        non_linearity: str = "swish",
-    ) -> None:
-        super().__init__()
-        out_channels = out_channels or in_channels
-
-        self.nonlinearity = get_act_fn(non_linearity)
-
-        self.norm1 = HunyuanVideo15RMS_norm(in_channels, images=False)
-        self.conv1 = HunyuanVideo15CausalConv3d(in_channels, out_channels, kernel_size=3)
-
-        self.norm2 = HunyuanVideo15RMS_norm(out_channels, images=False)
-        self.conv2 = HunyuanVideo15CausalConv3d(out_channels, out_channels, kernel_size=3)
-
-        self.nin_shortcut = None
-        if in_channels != out_channels:
-            self.nin_shortcut = nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        residual = hidden_states
-
-        hidden_states = self.norm1(hidden_states)
-        hidden_states = self.nonlinearity(hidden_states)
-        hidden_states = self.conv1(hidden_states)
-
-        hidden_states = self.norm2(hidden_states)
-        hidden_states = self.nonlinearity(hidden_states)
-        hidden_states = self.conv2(hidden_states)
-
-        if self.nin_shortcut is not None:
-            residual = self.nin_shortcut(residual)
-
-        return hidden_states + residual
-
-class SRResidualCausalBlock3D(nn.Module):
-    def __init__(self, channels: int):
-        super().__init__()
-        self.block = nn.Sequential(
-            HunyuanVideo15CausalConv3d(channels, channels, kernel_size=3),
-            nn.SiLU(inplace=True),
-            HunyuanVideo15CausalConv3d(channels, channels, kernel_size=3),
-            nn.SiLU(inplace=True),
-            HunyuanVideo15CausalConv3d(channels, channels, kernel_size=3),
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return x + self.block(x)
-
-
-class SRTo720pUpsampler(nn.Module):
-
-    def __init__(
-        self,
-        config: SRTo720pUpsamplerConfig,
-    ):
-        super().__init__()
-        self.in_conv = HunyuanVideo15CausalConv3d(config.in_channels, config.hidden_channels, kernel_size=3)
-        self.blocks = nn.ModuleList([SRResidualCausalBlock3D(config.hidden_channels) for _ in range(config.num_blocks)])
-        self.out_conv = HunyuanVideo15CausalConv3d(config.hidden_channels, config.out_channels, kernel_size=3)
-        self.global_residual = bool(config.global_residual)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        residual = x
-        y = self.in_conv(x)
-        for blk in self.blocks:
-            y = blk(y)
-        y = self.out_conv(y)
-        if self.global_residual and (y.shape == residual.shape):
-            y = y + residual
-        return y
-
-
-class SRTo1080pUpsampler(nn.Module):
-
-    def __init__(
-        self,
-        config: SRTo1080pUpsamplerConfig,
-    ):
-        super().__init__()
-        self.num_res_blocks = config.num_res_blocks
-        self.block_out_channels = config.block_out_channels
-        self.z_channels = config.z_channels
-
-        block_in = config.block_out_channels[0]
-        self.conv_in = HunyuanVideo15CausalConv3d(config.z_channels, block_in, kernel_size=3)
-
-        self.up = nn.ModuleList()
-        for i_level, ch in enumerate(config.block_out_channels):
-            block = nn.ModuleList()
-            block_out = ch
-            for _ in range(self.num_res_blocks + 1):
-                block.append(HunyuanVideo15ResnetBlock(in_channels=block_in, out_channels=block_out))
-                block_in = block_out
-            up = nn.Module()
-            up.block = block
-
-            self.up.append(up)
-
-        self.norm_out = HunyuanVideo15RMS_norm(block_in, images=False)
-        self.conv_out = HunyuanVideo15CausalConv3d(block_in, config.out_channels, kernel_size=3)
-
-        self.gradient_checkpointing = False
-        self.is_residual = config.is_residual
-
-    def forward(self, z: Tensor, target_shape: Sequence[int] = None) -> Tensor:
-        """
-        Args:
-            z: (B, C, T, H, W)
-            target_shape: (H, W)
-        """
-        if target_shape is not None and z.shape[-2:] != target_shape:
-            bsz = z.shape[0]
-            z = rearrange(z, "b c f h w -> (b f) c h w")
-            z = F.interpolate(z, size=target_shape, mode="bilinear", align_corners=False)
-            z = rearrange(z, "(b f) c h w -> b c f h w", b=bsz)
-
-        # z to block_in
-        repeats = self.block_out_channels[0] // (self.z_channels)
-        h = self.conv_in(z) + z.repeat_interleave(repeats=repeats, dim=1)
-
-        # upsampling
-        for i_level in range(len(self.block_out_channels)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.up[i_level].block[i_block](h)
-            if hasattr(self.up[i_level], "upsample"):
-                h = self.up[i_level].upsample(h)
-
-        # end
-        h = self.norm_out(h)
-        h = get_act_fn("swish")(h)
-        h = self.conv_out(h)
-        return h
@@ -700,7 +700,4 @@ class AutoencoderKLHunyuanVideo15(nn.Module, ParallelTiledVAE):
        else:
            z = posterior.mode()
        dec = self.decode(z)
-        return dec
-
-# Entry point for model registry
-EntryClass = AutoencoderKLHunyuanVideo15
+        return dec
@@ -850,6 +850,3 @@ class AutoencoderKLHunyuanVideo(nn.Module, ParallelTiledVAE):
            z = posterior.mode()
        dec = self.decode(z)
        return dec
-
-# Entry point for model registry
-EntryClass = AutoencoderKLHunyuanVideo
@@ -15,977 +15,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from einops import rearrange
-
-from fastvideo.layers.activation import get_act_fn
+from fastvideo.models.vaes.hunyuan15vae import AutoencoderKLHunyuanVideo15
 from fastvideo.configs.models.vaes import Hunyuan15VAEConfig
-from fastvideo.models.vaes.common import ParallelTiledVAE
-from fastvideo.models.vaes.hunyuan15vae import (
-    HunyuanVideo15RMS_norm as HYWorldRMS_norm,
-    HunyuanVideo15AttnBlock as HYWorldAttnBlock,
-)

-# Cache size for temporal feature caching (number of frames to cache)
-CACHE_T = 2
+class AutoencoderKLHYWorld(AutoencoderKLHunyuanVideo15):
+    # TODO(mingjia): add temporal caching support for HYWorld VAE

-class HYWorldCausalConv3d(nn.Module):
-    """Causal Conv3d with optional cache support for temporal feature caching."""
-    
    def __init__(
        self,
-        in_channels: int,
-        out_channels: int,
-        kernel_size: Union[int, Tuple[int, int, int]] = 3,
-        stride: Union[int, Tuple[int, int, int]] = 1,
-        padding: Union[int, Tuple[int, int, int]] = 0,
-        dilation: Union[int, Tuple[int, int, int]] = 1,
-        bias: bool = True,
-        pad_mode: str = "replicate",
+        config: Hunyuan15VAEConfig,
    ) -> None:
-        super().__init__()
+        AutoencoderKLHunyuanVideo15.__init__(self, config)

-        kernel_size = (kernel_size, kernel_size, kernel_size) if isinstance(kernel_size, int) else kernel_size
-
-        self.pad_mode = pad_mode
-        # Padding format: (W_left, W_right, H_left, H_right, T_left, T_right)
-        self.time_causal_padding = (
-            kernel_size[0] // 2,  # W_left (spatial)
-            kernel_size[0] // 2,  # W_right (spatial)
-            kernel_size[1] // 2,  # H_left (spatial)
-            kernel_size[1] // 2,  # H_right (spatial)
-            kernel_size[2] - 1,   # T_left (temporal causal padding)
-            0,                    # T_right (no future padding for causal)
-        )
-
-        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias)
-
-    def forward(self, hidden_states: torch.Tensor, cache_x: Optional[torch.Tensor] = None) -> torch.Tensor:
-        """
-        Forward pass with optional temporal caching.
-        
-        Args:
-            hidden_states: Input tensor of shape (B, C, T, H, W)
-            cache_x: Optional cached frames from previous chunk, shape (B, C, CACHE_T, H, W)
-                    When provided, uses cached frames instead of padding for temporal dimension.
-        """
-        padding = list(self.time_causal_padding)
-        
-        if cache_x is not None and self.time_causal_padding[4] > 0:  # Has temporal padding and cache
-            cache_x = cache_x.to(hidden_states.device)
-            # Concatenate cached frames with current input on temporal dimension
-            hidden_states = torch.cat([cache_x, hidden_states], dim=2)
-            # Reduce temporal padding since we have cached frames
-            padding[4] -= cache_x.shape[2]
-        
-        hidden_states = F.pad(hidden_states, padding, mode=self.pad_mode)
-        return self.conv(hidden_states)
-
-
-class HYWorldUpsample(nn.Module):
-    """Hierarchical upsampling with temporal/spatial support and optional caching."""
-    
-    def __init__(self, in_channels: int, out_channels: int, add_temporal_upsample: bool = True):
-        super().__init__()
-        factor = 2 * 2 * 2 if add_temporal_upsample else 1 * 2 * 2
-        self.conv = HYWorldCausalConv3d(in_channels, out_channels * factor, kernel_size=3)
-        self.add_temporal_upsample = add_temporal_upsample
-        self.repeats = factor * out_channels // in_channels
-
-    def forward(
-        self, 
-        x: torch.Tensor,
-        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
-        feat_idx: Optional[List[int]] = None,
-        first_chunk: bool = False,
-    ):
-        """
-        Forward pass with optional temporal caching.
-        
-        Args:
-            x: Input tensor of shape (B, C, T, H, W)
-            feat_cache: List of cached features for each conv layer
-            feat_idx: List containing current cache index [idx]
-            first_chunk: Whether this is the first chunk (affects temporal upsample behavior)
-        """
-        r1 = 2 if self.add_temporal_upsample else 1
-        if feat_cache is not None and feat_idx is not None:
-            idx = feat_idx[0]
-            cache_x = x[:, :, -CACHE_T:, :, :].clone()
-            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
-                cache_x = torch.cat(
-                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
-                    dim=2,
-                )
-            h = self.conv(x, feat_cache[idx])
-            feat_cache[idx] = cache_x
-            feat_idx[0] += 1
-        else:
-            h = self.conv(x)
-
-        if self.add_temporal_upsample:
-            if first_chunk:
-                # First chunk: only spatial upsample
-                h = rearrange(h, "b (r2 r3 c) f h w -> b c f (h r2) (w r3)", r2=2, r3=2)
-                h = h[:, : h.shape[1] // 2]
-                # Compute the shortcut part
-                shortcut = rearrange(x, "b (r2 r3 c) f h w -> b c f (h r2) (w r3)", r2=2, r3=2)
-                shortcut = shortcut.repeat_interleave(repeats=self.repeats // 2, dim=1)
-            elif feat_cache is None and x.shape[2] > 1:
-                # No cache and multiple frames: separate first frame and rest
-                h_first = h[:, :, :1, :, :]
-                h_rest = h[:, :, 1:, :, :]
-                x_first = x[:, :, :1, :, :]
-                x_rest = x[:, :, 1:, :, :]
-
-                # First frame: only spatial upsample
-                h_first = rearrange(h_first, "b (r2 r3 c) f h w -> b c f (h r2) (w r3)", r2=2, r3=2)
-                h_first = h_first[:, : h_first.shape[1] // 2]
-                shortcut_first = rearrange(x_first, "b (r2 r3 c) f h w -> b c f (h r2) (w r3)", r2=2, r3=2)
-                shortcut_first = shortcut_first.repeat_interleave(repeats=self.repeats // 2, dim=1)
-                out_first = h_first + shortcut_first
-
-                # Remaining frames: spatio-temporal upsample
-                h_rest = rearrange(h_rest, "b (r1 r2 r3 c) f h w -> b c (f r1) (h r2) (w r3)", r1=r1, r2=2, r3=2)
-                shortcut_rest = rearrange(x_rest, "b (r1 r2 r3 c) f h w -> b c (f r1) (h r2) (w r3)", r1=r1, r2=2, r3=2)
-                shortcut_rest = shortcut_rest.repeat_interleave(repeats=self.repeats, dim=1)
-                out_rest = h_rest + shortcut_rest
-
-                return torch.cat([out_first, out_rest], dim=2)
-            else:
-                # Subsequent chunks with cache: spatio-temporal upsample
-                h = rearrange(h, "b (r1 r2 r3 c) f h w -> b c (f r1) (h r2) (w r3)", r1=r1, r2=2, r3=2)
-                shortcut = rearrange(x, "b (r1 r2 r3 c) f h w -> b c (f r1) (h r2) (w r3)", r1=r1, r2=2, r3=2)
-                shortcut = shortcut.repeat_interleave(repeats=self.repeats, dim=1)
-        else:
-            h = rearrange(h, "b (r1 r2 r3 c) f h w -> b c (f r1) (h r2) (w r3)", r1=r1, r2=2, r3=2)
-            shortcut = x.repeat_interleave(repeats=self.repeats, dim=1)
-            shortcut = rearrange(shortcut, "b (r1 r2 r3 c) f h w -> b c (f r1) (h r2) (w r3)", r1=r1, r2=2, r3=2)
-        
-        return h + shortcut
-
-
-class HYWorldDownsample(nn.Module):
-    """Hierarchical downsampling with temporal/spatial support and optional caching."""
-    
-    def __init__(self, in_channels: int, out_channels: int, add_temporal_downsample: bool = True):
-        super().__init__()
-        factor = 2 * 2 * 2 if add_temporal_downsample else 1 * 2 * 2
-        self.conv = HYWorldCausalConv3d(in_channels, out_channels // factor, kernel_size=3)
-
-        self.add_temporal_downsample = add_temporal_downsample
-        self.group_size = factor * in_channels // out_channels
-
-    def forward(
-        self, 
-        x: torch.Tensor,
-        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
-        feat_idx: Optional[List[int]] = None,
-    ):
-        """
-        Forward pass with optional temporal caching.
-        
-        Args:
-            x: Input tensor of shape (B, C, T, H, W)
-            feat_cache: List of cached features for each conv layer
-            feat_idx: List containing current cache index [idx]
-        """
-        r1 = 2 if self.add_temporal_downsample else 1
-        
-        # Apply conv with caching
-        if feat_cache is not None and feat_idx is not None:
-            idx = feat_idx[0]
-            cache_x = x[:, :, -CACHE_T:, :, :].clone()
-            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
-                cache_x = torch.cat(
-                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
-                    dim=2,
-                )
-            h = self.conv(x, feat_cache[idx])
-            feat_cache[idx] = cache_x
-            feat_idx[0] += 1
-        else:
-            h = self.conv(x) # Change the channel, ready for spatial or temporal downsample
-        
-        if self.add_temporal_downsample:
-            if x.shape[2] == 1:
-                # Single frame: only spatial downsample
-                h = rearrange(h, "b c f (h r2) (w r3) -> b (r2 r3 c) f h w", r2=2, r3=2)
-                h = torch.cat([h, h], dim=1)
-                # Compute the shortcut part
-                shortcut = rearrange(x, "b c f (h r2) (w r3) -> b (r2 r3 c) f h w", r2=2, r3=2)
-                B, C, T, H, W = shortcut.shape
-                shortcut = shortcut.view(B, h.shape[1], self.group_size // 2, T, H, W).mean(dim=2)
-            else:
-                # Multiple frames: full spatio-temporal downsample
-                h = rearrange(h, "b c (f r1) (h r2) (w r3) -> b (r1 r2 r3 c) f h w", r1=r1, r2=2, r3=2)
-                # Shortcut computation
-                shortcut = rearrange(x, "b c (f r1) (h r2) (w r3) -> b (r1 r2 r3 c) f h w", r1=r1, r2=2, r3=2)
-                B, C, T, H, W = shortcut.shape
-                shortcut = shortcut.view(B, h.shape[1], self.group_size, T, H, W).mean(dim=2)
-        else:
-            h = rearrange(h, "b c (f r1) (h r2) (w r3) -> b (r1 r2 r3 c) f h w", r1=r1, r2=2, r3=2)
-            shortcut = rearrange(x, "b c (f r1) (h r2) (w r3) -> b (r1 r2 r3 c) f h w", r1=r1, r2=2, r3=2)
-            B, C, T, H, W = shortcut.shape
-            shortcut = shortcut.view(B, h.shape[1], self.group_size, T, H, W).mean(dim=2)
-
-        return h + shortcut
-
-
-class HYWorldResnetBlock(nn.Module):
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: Optional[int] = None,
-        non_linearity: str = "swish",
-    ) -> None:
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-
-        self.nonlinearity = get_act_fn(non_linearity)
-
-        self.norm1 = HYWorldRMS_norm(in_channels, images=False)
-        self.conv1 = HYWorldCausalConv3d(in_channels, out_channels, kernel_size=3)
-
-        self.norm2 = HYWorldRMS_norm(out_channels, images=False)
-        self.conv2 = HYWorldCausalConv3d(out_channels, out_channels, kernel_size=3)
-        if in_channels != out_channels:
-            self.nin_shortcut = nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-    
-    def forward(
-        self, 
-        hidden_states: torch.Tensor,
-        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
-        feat_idx: Optional[List[int]] = None,
-    ) -> torch.Tensor:
-        """
-        Forward pass with optional temporal feature caching.
-        
-        Args:
-            hidden_states: Input tensor of shape (B, C, T, H, W)
-            feat_cache: List of cached features for each conv layer
-            feat_idx: List containing current cache index [idx]
-        """
-        residual = hidden_states
-
-        hidden_states = self.norm1(hidden_states)
-        hidden_states = self.nonlinearity(hidden_states)
-        
-        # apply the feature cacheing mechanism
-        if feat_cache is not None and feat_idx is not None:
-            # Retrieve the current layer index.
-            idx = feat_idx[0]
-
-            # Clone the last CACHE_T frames from the current input to store for the next step.
-            cache_x = hidden_states[:, :, -CACHE_T:, :, :].clone()
-            
-            # Handle boundary conditions: if the current temporal chunk is too short (< 2 frames)
-            # and we have a previous cache, prepend the last frame of the previous cache.
-            # This ensures sufficient temporal context for the convolution kernel.
-            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
-                # actually this means chunk 1 after chunk 0
-                cache_x = torch.cat(
-                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
-                    dim=2,
-                )
-            
-            # Apply the convolution layer using `hidden_states`and the *previous* cached state.
-            hidden_states = self.conv1(hidden_states, feat_cache[idx])
-            
-            # Update the cache for this layer with the newly prepared context ('cache_x').
-            feat_cache[idx] = cache_x
-
-            # Increment the global layer index.
-            feat_idx[0] += 1
-        else:
-            hidden_states = self.conv1(hidden_states)
-
-        hidden_states = self.norm2(hidden_states)
-        hidden_states = self.nonlinearity(hidden_states)
-
-        # Second conv with caching
-        if feat_cache is not None and feat_idx is not None:
-            idx = feat_idx[0]
-            cache_x = hidden_states[:, :, -CACHE_T:, :, :].clone()
-            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
-                cache_x = torch.cat(
-                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
-                    dim=2,
-                )
-            hidden_states = self.conv2(hidden_states, feat_cache[idx])
-            feat_cache[idx] = cache_x
-            feat_idx[0] += 1
-        else:
-            hidden_states = self.conv2(hidden_states)
-
-        if self.in_channels != self.out_channels:
-            residual = self.nin_shortcut(residual)
-
-        return hidden_states + residual
-
-
-class HYWorldMidBlock(nn.Module):
-    """Mid block with attention and resnet blocks, with optional caching support."""
-    
-    def __init__(
-        self,
-        in_channels: int,
-        num_layers: int = 1,
-        add_attention: bool = True,
-    ) -> None:
-        super().__init__()
-        self.add_attention = add_attention
-
-        # There is always at least one resnet
-        resnets = [
-            HYWorldResnetBlock(
-                in_channels=in_channels,
-                out_channels=in_channels,
-            )
-        ]
-        attentions = []
-
-        for _ in range(num_layers):
-            if self.add_attention:
-                attentions.append(HYWorldAttnBlock(in_channels))
-            else:
-                attentions.append(None)
-
-            resnets.append(
-                HYWorldResnetBlock(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                )
-            )
-
-        self.attentions = nn.ModuleList(attentions)
-        self.resnets = nn.ModuleList(resnets)
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self, 
-        hidden_states: torch.Tensor,
-        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
-        feat_idx: Optional[List[int]] = None,
-    ) -> torch.Tensor:
-        """
-        Forward pass with optional temporal caching.
-        
-        Args:
-            hidden_states: Input tensor of shape (B, C, T, H, W)
-            feat_cache: List of cached features for each conv layer
-            feat_idx: List containing current cache index [idx]
-        """
-        hidden_states = self.resnets[0](hidden_states, feat_cache, feat_idx)
-
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            if attn is not None:
-                hidden_states = attn(hidden_states)
-            hidden_states = resnet(hidden_states, feat_cache, feat_idx)
-
-        return hidden_states
-
-
-class HYWorldDownBlock3D(nn.Module):
-    """Down block with resnet blocks and optional downsampling, with caching support."""
-    
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        num_layers: int = 1,
-        downsample_out_channels: Optional[int] = None,
-        add_temporal_downsample: int = True,
-    ) -> None:
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            in_channels = in_channels if i == 0 else out_channels
-            resnets.append(
-                HYWorldResnetBlock(
-                    in_channels=in_channels,
-                    out_channels=out_channels,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if downsample_out_channels is not None:
-            self.downsamplers = nn.ModuleList(
-                [
-                    HYWorldDownsample(
-                        out_channels,
-                        out_channels=downsample_out_channels,
-                        add_temporal_downsample=add_temporal_downsample,
-                    )
-                ]
-            )
-        else:
-            self.downsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self, 
-        hidden_states: torch.Tensor,
-        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
-        feat_idx: Optional[List[int]] = None,
-    ) -> torch.Tensor:
-        """
-        Forward pass with optional temporal caching.
-        
-        Args:
-            hidden_states: Input tensor of shape (B, C, T, H, W)
-            feat_cache: List of cached features for each conv layer
-            feat_idx: List containing current cache index [idx]
-        """
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states, feat_cache, feat_idx)
-
-        if self.downsamplers is not None:
-            for downsampler in self.downsamplers:
-                hidden_states = downsampler(hidden_states, feat_cache, feat_idx)
-
-        return hidden_states
-
-
-class HYWorldUpBlock3D(nn.Module):
-    """Up block with resnet blocks and optional upsampling, with caching support."""
-    
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        num_layers: int = 1,
-        upsample_out_channels: Optional[int] = None,
-        add_temporal_upsample: bool = True,
-    ) -> None:
-        super().__init__()
-        resnets = []
-
-        for i in range(num_layers):
-            input_channels = in_channels if i == 0 else out_channels
-
-            resnets.append(
-                HYWorldResnetBlock(
-                    in_channels=input_channels,
-                    out_channels=out_channels,
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-
-        if upsample_out_channels is not None:
-            self.upsamplers = nn.ModuleList(
-                [
-                    HYWorldUpsample(
-                        out_channels,
-                        out_channels=upsample_out_channels,
-                        add_temporal_upsample=add_temporal_upsample,
-                    )
-                ]
-            )
-        else:
-            self.upsamplers = None
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self, 
-        hidden_states: torch.Tensor,
-        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
-        feat_idx: Optional[List[int]] = None,
-        first_chunk: bool = False,
-    ) -> torch.Tensor:
-        """
-        Forward pass with optional temporal caching.
-        
-        Args:
-            hidden_states: Input tensor of shape (B, C, T, H, W)
-            feat_cache: List of cached features for each conv layer
-            feat_idx: List containing current cache index [idx]
-            first_chunk: Whether this is the first chunk (for upsampling behavior)
-        """
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for resnet in self.resnets:
-                hidden_states = self._gradient_checkpointing_func(resnet, hidden_states)
-        else:
-            for resnet in self.resnets:
-                hidden_states = resnet(hidden_states, feat_cache, feat_idx)
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states, feat_cache, feat_idx, first_chunk)
-
-        return hidden_states
-
-
-class HYWorldEncoder3D(nn.Module):
-    r"""
-    3D vae encoder for HunyuanImageRefiner with optional temporal caching.
-    """
-
-    def __init__(
-        self,
-        in_channels: int = 3,
-        out_channels: int = 64,
-        block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024, 1024),
-        layers_per_block: int = 2,
-        temporal_compression_ratio: int = 4,
-        spatial_compression_ratio: int = 16,
-        downsample_match_channel: bool = True,
-    ) -> None:
-        super().__init__()
-
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.group_size = block_out_channels[-1] // self.out_channels
-        self.block_out_channels = block_out_channels
-
-        self.conv_in = HYWorldCausalConv3d(in_channels, block_out_channels[0], kernel_size=3)
-        self.mid_block = None
-        self.down_blocks = nn.ModuleList([])
-
-        input_channel = block_out_channels[0]
-        for i in range(len(block_out_channels)):
-            add_spatial_downsample = i < np.log2(spatial_compression_ratio)
-            output_channel = block_out_channels[i]
-            if not add_spatial_downsample:
-                down_block = HYWorldDownBlock3D(
-                    num_layers=layers_per_block,
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    downsample_out_channels=None,
-                    add_temporal_downsample=False,
-                )
-                input_channel = output_channel
-            else:
-                add_temporal_downsample = i >= np.log2(spatial_compression_ratio // temporal_compression_ratio)
-                downsample_out_channels = block_out_channels[i + 1] if downsample_match_channel else output_channel
-                down_block = HYWorldDownBlock3D(
-                    num_layers=layers_per_block,
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    downsample_out_channels=downsample_out_channels,
-                    add_temporal_downsample=add_temporal_downsample,
-                )
-                input_channel = downsample_out_channels
-
-            self.down_blocks.append(down_block)
-
-        self.mid_block = HYWorldMidBlock(in_channels=block_out_channels[-1])
-
-        self.norm_out = HYWorldRMS_norm(block_out_channels[-1], images=False)
-        self.conv_act = nn.SiLU()
-        self.conv_out = HYWorldCausalConv3d(block_out_channels[-1], out_channels, kernel_size=3)
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self, 
-        hidden_states: torch.Tensor,
-        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
-        feat_idx: Optional[List[int]] = None,
-    ) -> torch.Tensor:
-        """
-        Forward pass with optional temporal caching.
-        
-        Args:
-            hidden_states: Input tensor of shape (B, C, T, H, W)
-            feat_cache: List of cached features for each conv layer
-            feat_idx: List containing current cache index [idx]
-        """
-        # conv_in with caching
-        if feat_cache is not None and feat_idx is not None:
-            idx = feat_idx[0]
-            cache_x = hidden_states[:, :, -CACHE_T:, :, :].clone()
-            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
-                cache_x = torch.cat(
-                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
-                    dim=2,
-                )
-            hidden_states = self.conv_in(hidden_states, feat_cache[idx])
-            feat_cache[idx] = cache_x
-            feat_idx[0] += 1
-        else:
-            hidden_states = self.conv_in(hidden_states)
-
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for down_block in self.down_blocks:
-                hidden_states = self._gradient_checkpointing_func(down_block, hidden_states)
-            hidden_states = self._gradient_checkpointing_func(self.mid_block, hidden_states)
-        else:
-            for down_block in self.down_blocks:
-                hidden_states = down_block(hidden_states, feat_cache, feat_idx)
-            hidden_states = self.mid_block(hidden_states, feat_cache, feat_idx)
-
-        batch_size, _, frame, height, width = hidden_states.shape
-        short_cut = hidden_states.view(batch_size, -1, self.group_size, frame, height, width).mean(dim=2)
-
-        hidden_states = self.norm_out(hidden_states)
-        hidden_states = self.conv_act(hidden_states)
-
-        # conv_out with caching
-        if feat_cache is not None and feat_idx is not None:
-            idx = feat_idx[0]
-            cache_x = hidden_states[:, :, -CACHE_T:, :, :].clone()
-            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
-                cache_x = torch.cat(
-                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
-                    dim=2,
-                )
-            hidden_states = self.conv_out(hidden_states, feat_cache[idx])
-            feat_cache[idx] = cache_x
-            feat_idx[0] += 1
-        else:
-            hidden_states = self.conv_out(hidden_states)
-
-        hidden_states += short_cut
-
-        return hidden_states
-
-
-class HYWorldDecoder3D(nn.Module):
-    r"""
-    Causal decoder for 3D video-like data used for HunyuanImage-1.5 Refiner with optional temporal caching.
-    """
-
-    def __init__(
-        self,
-        in_channels: int = 32,
-        out_channels: int = 3,
-        block_out_channels: Tuple[int, ...] = (1024, 1024, 512, 256, 128),
-        layers_per_block: int = 2,
-        spatial_compression_ratio: int = 16,
-        temporal_compression_ratio: int = 4,
-        upsample_match_channel: bool = True,
-    ):
-        super().__init__()
-        self.layers_per_block = layers_per_block
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.block_out_channels = block_out_channels
-        self.repeat = block_out_channels[0] // self.in_channels
-
-        self.conv_in = HYWorldCausalConv3d(self.in_channels, block_out_channels[0], kernel_size=3)
-        self.up_blocks = nn.ModuleList([])
-
-        # mid
-        self.mid_block = HYWorldMidBlock(in_channels=block_out_channels[0])
-
-        # up
-        input_channel = block_out_channels[0]
-        for i in range(len(block_out_channels)):
-            output_channel = block_out_channels[i]
-
-            add_spatial_upsample = i < np.log2(spatial_compression_ratio)
-            add_temporal_upsample = i < np.log2(temporal_compression_ratio)
-            if add_spatial_upsample or add_temporal_upsample:
-                upsample_out_channels = block_out_channels[i + 1] if upsample_match_channel else output_channel
-                up_block = HYWorldUpBlock3D(
-                    num_layers=self.layers_per_block + 1,
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    upsample_out_channels=upsample_out_channels,
-                    add_temporal_upsample=add_temporal_upsample,
-                )
-                input_channel = upsample_out_channels
-            else:
-                up_block = HYWorldUpBlock3D(
-                    num_layers=self.layers_per_block + 1,
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    upsample_out_channels=None,
-                    add_temporal_upsample=False,
-                )
-                input_channel = output_channel
-
-            self.up_blocks.append(up_block)
-
-        # out
-        self.norm_out = HYWorldRMS_norm(block_out_channels[-1], images=False)
-        self.conv_act = nn.SiLU()
-        self.conv_out = HYWorldCausalConv3d(block_out_channels[-1], out_channels, kernel_size=3)
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self, 
-        hidden_states: torch.Tensor,
-        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
-        feat_idx: Optional[List[int]] = None,
-        first_chunk: bool = False,
-    ) -> torch.Tensor:
-        """
-        Forward pass with optional temporal caching.
-        
-        Args:
-            hidden_states: Input tensor of shape (B, C, T, H, W)
-            feat_cache: List of cached features for each conv layer
-            feat_idx: List containing current cache index [idx]
-            first_chunk: Whether this is the first chunk (for upsampling behavior)
-        """
-        # conv_in with caching
-        if feat_cache is not None and feat_idx is not None:
-            idx = feat_idx[0]
-            cache_x = hidden_states[:, :, -CACHE_T:, :, :].clone()
-            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
-                cache_x = torch.cat(
-                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
-                    dim=2,
-                )
-            hidden_states = self.conv_in(hidden_states, feat_cache[idx]) + hidden_states.repeat_interleave(
-                repeats=self.repeat, dim=1
-            )
-            feat_cache[idx] = cache_x
-            feat_idx[0] += 1
-        else:
-            hidden_states = self.conv_in(hidden_states) + hidden_states.repeat_interleave(repeats=self.repeat, dim=1)
-
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            hidden_states = self._gradient_checkpointing_func(self.mid_block, hidden_states)
-            for up_block in self.up_blocks:
-                hidden_states = self._gradient_checkpointing_func(up_block, hidden_states)
-        else:
-            hidden_states = self.mid_block(hidden_states, feat_cache, feat_idx)
-            for up_block in self.up_blocks:
-                hidden_states = up_block(hidden_states, feat_cache, feat_idx, first_chunk)
-
-        # post-process
-        hidden_states = self.norm_out(hidden_states)
-        hidden_states = self.conv_act(hidden_states)
-
-        # conv_out with caching
-        if feat_cache is not None and feat_idx is not None:
-            idx = feat_idx[0]
-            cache_x = hidden_states[:, :, -CACHE_T:, :, :].clone()
-            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
-                cache_x = torch.cat(
-                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
-                    dim=2,
-                )
-            hidden_states = self.conv_out(hidden_states, feat_cache[idx])
-            feat_cache[idx] = cache_x
-            feat_idx[0] += 1
-        else:
-            hidden_states = self.conv_out(hidden_states)
-        
-        return hidden_states
-
-
-class AutoencoderKLHYWorld(nn.Module, ParallelTiledVAE):
-    r"""
-    A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
-    Revised from HunyuanVideo-1.5 with temporal caching support for HY-WorldPlay integration.
-    """
-
-    _supports_gradient_checkpointing = True
-
-    def __init__(
-        self,
-        config: Hunyuan15VAEConfig, # HYWorld use the same VAE architecture as HunyuanVideo-1.5
-    ) -> None:
-        nn.Module.__init__(self)
-        ParallelTiledVAE.__init__(self, config)
-
-        self.encoder: Optional[HYWorldEncoder3D] = None
-        self.decoder: Optional[HYWorldDecoder3D] = None
-
-        if config.load_encoder:
-            self.encoder = HYWorldEncoder3D(
-                in_channels=config.in_channels,
-                out_channels=config.latent_channels * 2,
-                block_out_channels=config.block_out_channels,
-                layers_per_block=config.layers_per_block,
-                temporal_compression_ratio=config.temporal_compression_ratio,
-                spatial_compression_ratio=config.spatial_compression_ratio,
-                downsample_match_channel=config.downsample_match_channel,
-            )
-
-        if config.load_decoder:
-            self.decoder = HYWorldDecoder3D(
-                in_channels=config.latent_channels,
-                out_channels=config.out_channels,
-                block_out_channels=list(reversed(config.block_out_channels)),
-                layers_per_block=config.layers_per_block,
-                temporal_compression_ratio=config.temporal_compression_ratio,
-                spatial_compression_ratio=config.spatial_compression_ratio,
-                upsample_match_channel=config.upsample_match_channel,
-            )
-
-        # TODO: Add spatial tiling.
-        self.use_tiling = False
-
-        # The minimal tile height and width for spatial tiling to be used
-        self.tile_sample_min_height = 256
-        self.tile_sample_min_width = 256
-        self.tile_sample_min_num_frames = 2000 # Fill in a random large number, as hy1.5 vae does not use temporal tiling
-
-        # Cache-related attributes (initialized in clear_cache)
-        self._conv_num: int = 0
-        self._conv_idx: List[int] = [0]
-        self._feat_map: List[Optional[torch.Tensor]] = []
-        self._enc_conv_num: int = 0
-        self._enc_conv_idx: List[int] = [0]
-        self._enc_feat_map: List[Optional[torch.Tensor]] = []
-
-        # Precompute and cache conv counts for encoder and decoder for clear_cache speedup
-        self._cached_conv_counts = {
-            "decoder": (
-                sum(1 for m in self.decoder.modules() if isinstance(m, HYWorldCausalConv3d))
-                if self.decoder is not None
-                else 0
-            ),
-            "encoder": (
-                sum(1 for m in self.encoder.modules() if isinstance(m, HYWorldCausalConv3d))
-                if self.encoder is not None
-                else 0
-            ),
-        }
-
-    def clear_cache(self) -> None:
-        """
-        Initialize/clear the feature cache for chunk-based encoding/decoding.
-        
-        This should be called before starting a new encode/decode sequence to ensure
-        the cache is properly initialized.
-        """
-        # Cache for decoder
-        self._conv_num = self._cached_conv_counts["decoder"]
-        self._conv_idx = [0]
-        self._feat_map: List[Optional[torch.Tensor]] = [None] * self._conv_num
-        
-        # Cache for encoder
-        self._enc_conv_num = self._cached_conv_counts["encoder"]
-        self._enc_conv_idx = [0]
-        self._enc_feat_map: List[Optional[torch.Tensor]] = [None] * self._enc_conv_num
-
-    def _encode(self, x: torch.Tensor) -> torch.Tensor:
-        """
-        Encode video with temporal caching for chunk-based processing.
-        
-        This processes video in chunks (first frame separately, then 4 frames at a time)
-        while maintaining temporal context through caching. This matches the HY-WorldPlay
-        behavior for memory-efficient long video encoding.
-        
-        Args:
-            x: Input video tensor of shape (B, C, T, H, W)
-            
-        Returns:
-            Encoded latent tensor
-        """
-        assert self.encoder is not None, "Encoder not loaded"
-        _, _, num_frame, _, _ = x.shape
-
-        self.clear_cache()
-        
-        # Process in chunks: first frame alone, then groups of 4 frames
-        iter_ = 1 + (num_frame - 1) // 4
-        for i in range(iter_):
-            self._enc_conv_idx = [0]
-            if i == 0:
-                # First frame
-                out = self.encoder(
-                    x[:, :, :1, :, :],
-                    feat_cache=self._enc_feat_map,
-                    feat_idx=self._enc_conv_idx,
-                )
-            else:
-                # Subsequent frames in groups of 4
-                out_ = self.encoder(
-                    x[:, :, 1 + 4 * (i - 1) : 1 + 4 * i, :, :],
-                    feat_cache=self._enc_feat_map,
-                    feat_idx=self._enc_conv_idx,
-                )
-                out = torch.cat([out, out_], dim=2)
-
-        self.clear_cache()
-        return out
-
-    def _decode(self, z: torch.Tensor) -> torch.Tensor:
-        """
-        Decode latents with temporal caching for chunk-based processing.
-        
-        This processes latents one frame at a time while maintaining temporal context
-        through caching. This matches the HY-WorldPlay behavior for memory-efficient
-        long video decoding.
-        
-        Args:
-            z: Latent tensor of shape (B, C, T, H, W)
-            
-        Returns:
-            Decoded video tensor
-        """
-        assert self.decoder is not None, "Decoder not loaded"
-        _, _, num_frame, _, _ = z.shape
-
-        self.clear_cache()
-        
-        # Process one frame at a time with caching
-        for i in range(num_frame):
-            self._conv_idx = [0]
-            if i == 0:
-                # First frame with first_chunk=True
-                out = self.decoder(
-                    z[:, :, i : i + 1, :, :],
-                    feat_cache=self._feat_map,
-                    feat_idx=self._conv_idx,
-                    first_chunk=True,
-                )
-            else:
-                # Subsequent frames
-                out_ = self.decoder(
-                    z[:, :, i : i + 1, :, :],
-                    feat_cache=self._feat_map,
-                    feat_idx=self._conv_idx,
-                    first_chunk=False,
-                )
-                out = torch.cat([out, out_], dim=2)
-
-        self.clear_cache()
-        return out
-
-    def forward(
-        self,
-        sample: torch.Tensor,
-        sample_posterior: bool = False,
-        return_dict: bool = True,
-        generator: Optional[torch.Generator] = None,
-    ) -> torch.Tensor:
-        r"""
-        Args:
-            sample (`torch.Tensor`): Input sample.
-            sample_posterior (`bool`, *optional*, defaults to `False`):
-                Whether to sample from the posterior.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
-            generator (`torch.Generator`, *optional*):
-                Generator for sampling.
-        """
-        x = sample
-
-        # encode() uses temporal caching by default (via _encode)
-        posterior = self.encode(x)
-
-        if sample_posterior:
-            z = posterior.sample(generator=generator)
-        else:
-            z = posterior.mode()
-
-        # decode() uses temporal caching by default (via _decode)
-        dec = self.decode(z)
-
-        return dec
-
-# Entry point for model registry
-EntryClass = AutoencoderKLHYWorld
@@ -1802,6 +1802,3 @@ class LTX2CausalVideoAutoencoder(nn.Module):
        if previous_chunk is not None:
            previous_weights = previous_weights.clamp(min=1e-8)
            yield previous_chunk / previous_weights
-
-# Entry point for model registry
-EntryClass = LTX2CausalVideoAutoencoder
@@ -1138,6 +1138,3 @@ class AutoencoderKLStepvideo(nn.Module, ParallelTiledVAE):
            z = posterior.mode()
        dec = self.decode(z)
        return dec
-
-# Entry point for model registry
-EntryClass = AutoencoderKLStepvideo
@@ -1319,5 +1319,3 @@ class AutoencoderKLWan(nn.Module, ParallelTiledVAE):
        dec = self.decode(z)
        return dec

-# Entry point for model registry
-EntryClass = AutoencoderKLWan
@@ -277,7 +277,7 @@ def load_video(
    if convert_method is not None:
        pil_images = convert_method(pil_images)

-    return (pil_images, original_fps) if return_fps else pil_images
+    return pil_images, original_fps if return_fps else pil_images


 def get_default_height_width(
@@ -12,9 +12,10 @@ from fastvideo.logger import init_logger
 from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
 from fastvideo.pipelines.lora_pipeline import LoRAPipeline
 from fastvideo.pipelines.pipeline_batch_info import ForwardBatch, TrainingBatch
-from fastvideo.pipelines.pipeline_registry import PipelineType
-from fastvideo.registry import get_model_info
-from fastvideo.utils import maybe_download_model
+from fastvideo.pipelines.pipeline_registry import (PipelineType,
+                                                   get_pipeline_registry)
+from fastvideo.utils import (maybe_download_model,
+                             verify_model_config_and_directory)

 logger = init_logger(__name__)

@@ -41,17 +42,30 @@ def build_pipeline(
    # fastvideo_args.downloaded_model_path = model_path
    logger.info("Model path: %s", model_path)

+    config = verify_model_config_and_directory(model_path)
+    pipeline_name = config.get("_class_name")
+
+    if fastvideo_args.override_pipeline_cls_name:
+        logger.info("Overriding pipeline class name from %s to %s",
+                    pipeline_name, fastvideo_args.override_pipeline_cls_name)
+        pipeline_name = fastvideo_args.override_pipeline_cls_name
+
+    if pipeline_name is None:
+        raise ValueError(
+            "Model config does not contain a _class_name attribute. "
+            "Only diffusers format is supported.")
+
+    # Get the appropriate pipeline registry based on pipeline_type
    logger.info(
        "Building pipeline of type: %s", pipeline_type.value if isinstance(
            pipeline_type, PipelineType) else pipeline_type)
+    pipeline_registry = get_pipeline_registry(pipeline_type)

-    model_info = get_model_info(
-        model_path=model_path,
-        pipeline_type=pipeline_type,
-        workload_type=fastvideo_args.workload_type,
-        override_pipeline_cls_name=fastvideo_args.override_pipeline_cls_name,
-    )
-    pipeline_cls = model_info.pipeline_cls
+    if isinstance(pipeline_type, str):
+        pipeline_type = PipelineType.from_string(pipeline_type)
+
+    pipeline_cls = pipeline_registry.resolve_pipeline_cls(
+        pipeline_name, pipeline_type, fastvideo_args.workload_type)

    # instantiate the pipelines
    pipeline = pipeline_cls(model_path, fastvideo_args)
@@ -5,8 +5,8 @@ from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.logger import init_logger
 from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
 from fastvideo.pipelines.stages import (ConditioningStage,
-                                        Cosmos25AutoDenoisingStage,
-                                        Cosmos25AutoLatentPreparationStage,
+                                        Cosmos25DenoisingStage,
+                                        Cosmos25LatentPreparationStage,
                                        DecodingStage, InputValidationStage,
                                        Cosmos25TextEncodingStage,
                                        Cosmos25TimestepPreparationStage)
@@ -42,13 +42,13 @@ class Cosmos2_5Pipeline(ComposedPipelineBase):
                           scheduler=self.get_module("scheduler")))

        self.add_stage(stage_name="latent_preparation_stage",
-                       stage=Cosmos25AutoLatentPreparationStage(
+                       stage=Cosmos25LatentPreparationStage(
                           scheduler=self.get_module("scheduler"),
                           transformer=self.get_module("transformer"),
                           vae=self.get_module("vae")))

        self.add_stage(stage_name="denoising_stage",
-                       stage=Cosmos25AutoDenoisingStage(
+                       stage=Cosmos25DenoisingStage(
                           transformer=self.get_module("transformer"),
                           scheduler=self.get_module("scheduler")))

@@ -1,162 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Hunyuan video diffusion pipeline implementation.
-
-This module contains an implementation of the Hunyuan video diffusion pipeline
-using the modular pipeline architecture.
-"""
-import torch
-import time
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
-from fastvideo.pipelines.stages import (
-    ConditioningStage, DecodingStage, DenoisingStage, InputValidationStage,
-    LatentPreparationStage, TextEncodingStage, TimestepPreparationStage,
-    Hy15ImageEncodingStage, SRDenoisingStage)
-from fastvideo.distributed.parallel_state import get_local_torch_device
-
-# TODO(will): move PRECISION_TO_TYPE to better place
-
-logger = init_logger(__name__)
-
-
-class HunyuanVideo152SRPipeline(ComposedPipelineBase):
-
-    _required_config_modules = [
-        "text_encoder", "text_encoder_2", "tokenizer", "tokenizer_2", "vae",
-        "transformer", "transformer_2", "transformer_3", "scheduler",
-        "upsampler", "upsampler_2"
-    ]
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
-        """Set up pipeline stages with proper dependency injection."""
-
-        self.add_stage(stage_name="input_validation_stage",
-                       stage=InputValidationStage())
-
-        self.add_stage(stage_name="prompt_encoding_stage_primary",
-                       stage=TextEncodingStage(
-                           text_encoders=[
-                               self.get_module("text_encoder"),
-                               self.get_module("text_encoder_2")
-                           ],
-                           tokenizers=[
-                               self.get_module("tokenizer"),
-                               self.get_module("tokenizer_2")
-                           ],
-                       ))
-
-        self.add_stage(stage_name="conditioning_stage",
-                       stage=ConditioningStage())
-
-        self.add_stage(stage_name="timestep_preparation_stage",
-                       stage=TimestepPreparationStage(
-                           scheduler=self.get_module("scheduler")))
-
-        self.add_stage(stage_name="latent_preparation_stage",
-                       stage=LatentPreparationStage(
-                           scheduler=self.get_module("scheduler"),
-                           transformer=self.get_module("transformer")))
-
-        self.add_stage(stage_name="image_encoding_stage",
-                       stage=Hy15ImageEncodingStage(image_encoder=None,
-                                                    image_processor=None))
-
-        self.add_stage(stage_name="denoising_stage",
-                       stage=DenoisingStage(
-                           transformer=self.get_module("transformer"),
-                           scheduler=self.get_module("scheduler")))
-
-        self.add_stage(stage_name="sr_720p_latent_preparation_stage",
-                       stage=LatentPreparationStage(
-                           scheduler=self.get_module("scheduler"),
-                           transformer=self.get_module("transformer_2")))
-
-        self.add_stage(stage_name="sr_720p_denoising_stage",
-                       stage=SRDenoisingStage(
-                           transformer=self.get_module("transformer_2"),
-                           scheduler=self.get_module("scheduler"),
-                           upsampler=self.get_module("upsampler")))
-
-        self.add_stage(stage_name="sr_1080p_latent_preparation_stage",
-                       stage=LatentPreparationStage(
-                           scheduler=self.get_module("scheduler"),
-                           transformer=self.get_module("transformer_3")))
-
-        self.add_stage(stage_name="sr_1080p_denoising_stage",
-                       stage=SRDenoisingStage(
-                           transformer=self.get_module("transformer_3"),
-                           scheduler=self.get_module("scheduler"),
-                           upsampler=self.get_module("upsampler_2")))
-
-        self.add_stage(stage_name="decoding_stage",
-                       stage=DecodingStage(vae=self.get_module("vae")))
-
-    @torch.no_grad()
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        """
-        Generate a video or image using the pipeline.
-        
-        Args:
-            batch: The batch to generate from.
-            fastvideo_args: The inference arguments.
-        Returns:
-            ForwardBatch: The batch with the generated video or image.
-        """
-        if not self.post_init_called:
-            self.post_init()
-
-        self.get_module("transformer").to(get_local_torch_device())
-        # Execute each stage
-        logger.info("Running pipeline stages: %s",
-                    self._stage_name_mapping.keys())
-        # logger.info("Batch: %s", batch)
-        batch = self.input_validation_stage(batch, fastvideo_args)
-        batch = self.prompt_encoding_stage_primary(batch, fastvideo_args)
-        batch = self.conditioning_stage(batch, fastvideo_args)
-        batch = self.timestep_preparation_stage(batch, fastvideo_args)
-        batch = self.latent_preparation_stage(batch, fastvideo_args)
-        batch = self.image_encoding_stage(batch, fastvideo_args)
-        batch = self.denoising_stage(batch, fastvideo_args)
-        self.get_module("transformer").to("cpu")
-
-        # 720p SR
-        self.get_module("transformer_2").to(get_local_torch_device())
-        batch.lq_latents = batch.latents
-        batch.latents = None
-        batch.height = 720
-        batch.width = 1280
-        batch.num_inference_steps_sr = 6
-        batch = self.sr_720p_latent_preparation_stage(batch, fastvideo_args)
-        batch = self.image_encoding_stage(batch, fastvideo_args)
-        batch = self.sr_720p_denoising_stage(batch, fastvideo_args)
-        self.get_module("transformer_2").to("cpu")
-
-        # 1080p SR
-        self.get_module("transformer_3").to(get_local_torch_device())
-        batch.lq_latents = batch.latents
-        batch.latents = None
-        batch.height = 1072
-        batch.width = 1920
-        batch.num_inference_steps_sr = 8
-        batch = self.sr_1080p_latent_preparation_stage(batch, fastvideo_args)
-        batch = self.image_encoding_stage(batch, fastvideo_args)
-        batch = self.sr_1080p_denoising_stage(batch, fastvideo_args)
-        self.get_module("transformer_3").to("cpu")
-
-        start_time = time.time()
-        batch = self.decoding_stage(batch, fastvideo_args)
-        end_time = time.time()
-        logger.info("Decoding time: %s seconds", end_time - start_time)
-
-        # Return the output
-        return batch
-
-
-EntryClass = HunyuanVideo152SRPipeline
@@ -1,74 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Hunyuan video diffusion pipeline implementation.
-
-This module contains an implementation of the Hunyuan video diffusion pipeline
-using the modular pipeline architecture.
-"""
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
-from fastvideo.pipelines.stages import (ConditioningStage, DecodingStage,
-                                        DenoisingStage, InputValidationStage,
-                                        LatentPreparationStage,
-                                        TextEncodingStage,
-                                        TimestepPreparationStage,
-                                        Hy15ImageEncodingStage)
-
-# TODO(will): move PRECISION_TO_TYPE to better place
-
-logger = init_logger(__name__)
-
-
-class HunyuanVideo15ImageToVideoPipeline(ComposedPipelineBase):
-
-    _required_config_modules = [
-        "text_encoder", "text_encoder_2", "tokenizer", "tokenizer_2", "vae",
-        "transformer", "scheduler"
-    ]
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
-        """Set up pipeline stages with proper dependency injection."""
-
-        self.add_stage(stage_name="input_validation_stage",
-                       stage=InputValidationStage())
-
-        self.add_stage(stage_name="prompt_encoding_stage_primary",
-                       stage=TextEncodingStage(
-                           text_encoders=[
-                               self.get_module("text_encoder"),
-                               self.get_module("text_encoder_2")
-                           ],
-                           tokenizers=[
-                               self.get_module("tokenizer"),
-                               self.get_module("tokenizer_2")
-                           ],
-                       ))
-
-        self.add_stage(stage_name="conditioning_stage",
-                       stage=ConditioningStage())
-
-        self.add_stage(stage_name="timestep_preparation_stage",
-                       stage=TimestepPreparationStage(
-                           scheduler=self.get_module("scheduler")))
-
-        self.add_stage(stage_name="latent_preparation_stage",
-                       stage=LatentPreparationStage(
-                           scheduler=self.get_module("scheduler"),
-                           transformer=self.get_module("transformer")))
-
-        self.add_stage(stage_name="image_encoding_stage",
-                       stage=Hy15ImageEncodingStage(image_encoder=None,
-                                                    image_processor=None))
-
-        self.add_stage(stage_name="denoising_stage",
-                       stage=DenoisingStage(
-                           transformer=self.get_module("transformer"),
-                           scheduler=self.get_module("scheduler")))
-
-        self.add_stage(stage_name="decoding_stage",
-                       stage=DecodingStage(vae=self.get_module("vae")))
-
-
-EntryClass = HunyuanVideo15ImageToVideoPipeline
@@ -1,136 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Hunyuan video diffusion pipeline implementation.
-
-This module contains an implementation of the Hunyuan video diffusion pipeline
-using the modular pipeline architecture.
-"""
-import torch
-import time
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
-from fastvideo.pipelines.stages import (
-    ConditioningStage, DecodingStage, DenoisingStage, InputValidationStage,
-    LatentPreparationStage, TextEncodingStage, TimestepPreparationStage,
-    Hy15ImageEncodingStage, SRDenoisingStage)
-from fastvideo.distributed.parallel_state import get_local_torch_device
-
-# TODO(will): move PRECISION_TO_TYPE to better place
-
-logger = init_logger(__name__)
-
-
-class HunyuanVideo15SRPipeline(ComposedPipelineBase):
-
-    _required_config_modules = [
-        "text_encoder", "text_encoder_2", "tokenizer", "tokenizer_2", "vae",
-        "transformer", "transformer_2", "scheduler", "upsampler"
-    ]
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
-        """Set up pipeline stages with proper dependency injection."""
-
-        self.add_stage(stage_name="input_validation_stage",
-                       stage=InputValidationStage())
-
-        self.add_stage(stage_name="prompt_encoding_stage_primary",
-                       stage=TextEncodingStage(
-                           text_encoders=[
-                               self.get_module("text_encoder"),
-                               self.get_module("text_encoder_2")
-                           ],
-                           tokenizers=[
-                               self.get_module("tokenizer"),
-                               self.get_module("tokenizer_2")
-                           ],
-                       ))
-
-        self.add_stage(stage_name="conditioning_stage",
-                       stage=ConditioningStage())
-
-        self.add_stage(stage_name="timestep_preparation_stage",
-                       stage=TimestepPreparationStage(
-                           scheduler=self.get_module("scheduler")))
-
-        self.add_stage(stage_name="latent_preparation_stage",
-                       stage=LatentPreparationStage(
-                           scheduler=self.get_module("scheduler"),
-                           transformer=self.get_module("transformer")))
-
-        self.add_stage(stage_name="image_encoding_stage",
-                       stage=Hy15ImageEncodingStage(image_encoder=None,
-                                                    image_processor=None))
-
-        self.add_stage(stage_name="denoising_stage",
-                       stage=DenoisingStage(
-                           transformer=self.get_module("transformer"),
-                           scheduler=self.get_module("scheduler")))
-
-        self.add_stage(stage_name="sr_latent_preparation_stage",
-                       stage=LatentPreparationStage(
-                           scheduler=self.get_module("scheduler"),
-                           transformer=self.get_module("transformer_2")))
-
-        self.add_stage(stage_name="sr_denoising_stage",
-                       stage=SRDenoisingStage(
-                           transformer=self.get_module("transformer_2"),
-                           scheduler=self.get_module("scheduler"),
-                           upsampler=self.get_module("upsampler")))
-
-        self.add_stage(stage_name="decoding_stage",
-                       stage=DecodingStage(vae=self.get_module("vae")))
-
-    @torch.no_grad()
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        """
-        Generate a video or image using the pipeline.
-        
-        Args:
-            batch: The batch to generate from.
-            fastvideo_args: The inference arguments.
-        Returns:
-            ForwardBatch: The batch with the generated video or image.
-        """
-        if not self.post_init_called:
-            self.post_init()
-
-        # Execute each stage
-        logger.info("Running pipeline stages: %s",
-                    self._stage_name_mapping.keys())
-        # logger.info("Batch: %s", batch)
-        self.get_module("transformer").to(get_local_torch_device())
-        batch = self.input_validation_stage(batch, fastvideo_args)
-        batch = self.prompt_encoding_stage_primary(batch, fastvideo_args)
-        batch = self.conditioning_stage(batch, fastvideo_args)
-        batch = self.timestep_preparation_stage(batch, fastvideo_args)
-        batch = self.latent_preparation_stage(batch, fastvideo_args)
-        batch = self.image_encoding_stage(batch, fastvideo_args)
-        batch = self.denoising_stage(batch, fastvideo_args)
-        self.get_module("transformer").to("cpu")
-
-        self.get_module("transformer_2").to(get_local_torch_device())
-        batch.lq_latents = batch.latents
-        batch.latents = None
-        batch.height = batch.height_sr
-        batch.width = batch.width_sr
-        batch = self.sr_latent_preparation_stage(batch, fastvideo_args)
-        batch = self.image_encoding_stage(batch, fastvideo_args)
-        batch = self.sr_denoising_stage(batch, fastvideo_args)
-        self.get_module("transformer_2").to("cpu")
-
-        start_time = time.time()
-        batch = self.decoding_stage(batch, fastvideo_args)
-        end_time = time.time()
-        logger.info("Decoding time: %s seconds", end_time - start_time)
-
-        # Return the output
-        return batch
-
-
-EntryClass = HunyuanVideo15SRPipeline
@@ -16,7 +16,7 @@ from fastvideo.pipelines.stages import (DecodingStage, InputValidationStage,
                                        LTX2AudioDecodingStage,
                                        LTX2DenoisingStage,
                                        LTX2LatentPreparationStage,
-                                        LTX2TextEncodingStage)
+                                        TextEncodingStage)

 logger = init_logger(__name__)

@@ -40,7 +40,7 @@ class LTX2Pipeline(ComposedPipelineBase):

        self.add_stage(
            stage_name="prompt_encoding_stage",
-            stage=LTX2TextEncodingStage(
+            stage=TextEncodingStage(
                text_encoders=[self.get_module("text_encoder")],
                tokenizers=[self.get_module("tokenizer")],
            ),
@@ -96,28 +96,47 @@ class MatrixGameCausalDMDPipeline(LoRAPipeline, ComposedPipelineBase):
        self._vae_cache = None

    def streaming_step(self, keyboard_action, mouse_action) -> ForwardBatch:
+        import time
        denoiser = self._stage_name_mapping["denoising_stage"]
        ctx = denoiser._streaming_ctx
        assert ctx is not None, "streaming_ctx must be set"

        start_idx = ctx.start_index
+
+        # Time DiT forward pass
+        torch.cuda.synchronize()
+        t0 = time.perf_counter()
        batch = denoiser.streaming_step(keyboard_action, mouse_action)
+        torch.cuda.synchronize()
+        dit_time_ms = (time.perf_counter() - t0) * 1000
+
        end_idx = ctx.start_index

        # Decode only the new generated block
+        vae_time_ms = 0.0
        if end_idx > start_idx:
            current_latents = batch.latents[:, :, start_idx:end_idx, :, :]
            args = ctx.fastvideo_args
            decoder = self._stage_name_mapping["decoding_stage"]
+
+            # Time VAE decode
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
            decoded_frames, self._vae_cache = decoder.streaming_decode(
                current_latents,
                args,
                cache=self._vae_cache,
                is_first_chunk=(start_idx == 0))
+            torch.cuda.synchronize()
+            vae_time_ms = (time.perf_counter() - t0) * 1000
+
            batch.output = decoded_frames
        else:
            batch.output = None

+        # Store timings in batch for caller to access
+        batch.stage_timings = {"dit_ms": dit_time_ms, "vae_ms": vae_time_ms}
+
        return batch

    def streaming_clear(self) -> None:
@@ -0,0 +1,122 @@
+# SPDX-License-Identifier: Apache-2.0
+"""WanGame causal DMD pipeline implementation."""
+
+from fastvideo.fastvideo_args import FastVideoArgs
+import torch
+from fastvideo.logger import init_logger
+from fastvideo.pipelines import ComposedPipelineBase, ForwardBatch, LoRAPipeline
+
+from fastvideo.pipelines.stages import (ConditioningStage, DecodingStage,
+                                        InputValidationStage,
+                                        LatentPreparationStage,
+                                        TextEncodingStage,
+                                        MatrixGameCausalDenoisingStage)
+from fastvideo.pipelines.stages.image_encoding import (
+    MatrixGameImageEncodingStage, MatrixGameImageVAEEncodingStage)
+
+logger = init_logger(__name__)
+
+
+class WanGameCausalDMDPipeline(LoRAPipeline, ComposedPipelineBase):
+    _required_config_modules = [
+        "vae", "transformer", "scheduler", "image_encoder", "image_processor"
+    ]
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
+        self.add_stage(stage_name="input_validation_stage",
+                       stage=InputValidationStage())
+
+        if (self.get_module("text_encoder", None) is not None
+                and self.get_module("tokenizer", None) is not None):
+            self.add_stage(stage_name="prompt_encoding_stage",
+                           stage=TextEncodingStage(
+                               text_encoders=[self.get_module("text_encoder")],
+                               tokenizers=[self.get_module("tokenizer")],
+                           ))
+
+        if (self.get_module("image_encoder", None) is not None
+                and self.get_module("image_processor", None) is not None):
+            self.add_stage(
+                stage_name="image_encoding_stage",
+                stage=MatrixGameImageEncodingStage(
+                    image_encoder=self.get_module("image_encoder"),
+                    image_processor=self.get_module("image_processor"),
+                ))
+
+        self.add_stage(stage_name="conditioning_stage",
+                       stage=ConditioningStage())
+
+        self.add_stage(stage_name="latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer", None)))
+
+        self.add_stage(
+            stage_name="image_latent_preparation_stage",
+            stage=MatrixGameImageVAEEncodingStage(vae=self.get_module("vae")))
+
+        self.add_stage(stage_name="denoising_stage",
+                       stage=MatrixGameCausalDenoisingStage(
+                           transformer=self.get_module("transformer"),
+                           transformer_2=self.get_module("transformer_2", None),
+                           scheduler=self.get_module("scheduler"),
+                           pipeline=self,
+                           vae=self.get_module("vae")))
+
+        self.add_stage(stage_name="decoding_stage",
+                       stage=DecodingStage(vae=self.get_module("vae")))
+
+        logger.info("WanGameCausalDMDPipeline initialized")
+
+    @torch.no_grad()
+    def streaming_reset(self, batch: ForwardBatch,
+                        fastvideo_args: FastVideoArgs):
+        if not self.post_init_called:
+            self.post_init()
+
+        stages_to_run = [
+            "input_validation_stage", "prompt_encoding_stage",
+            "image_encoding_stage", "conditioning_stage",
+            "latent_preparation_stage", "image_latent_preparation_stage"
+        ]
+
+        for stage_name in stages_to_run:
+            if stage_name in self._stage_name_mapping:
+                batch = self._stage_name_mapping[stage_name].forward(
+                    batch, fastvideo_args)
+
+        denoiser = self._stage_name_mapping["denoising_stage"]
+        denoiser.streaming_reset(batch, fastvideo_args)
+        self._vae_cache = None
+
+    def streaming_step(self, keyboard_action, mouse_action) -> ForwardBatch:
+        denoiser = self._stage_name_mapping["denoising_stage"]
+        ctx = denoiser._streaming_ctx
+        assert ctx is not None, "streaming_ctx must be set"
+
+        start_idx = ctx.start_index
+        batch = denoiser.streaming_step(keyboard_action, mouse_action)
+        end_idx = ctx.start_index
+
+        if end_idx > start_idx:
+            current_latents = batch.latents[:, :, start_idx:end_idx, :, :]
+            args = ctx.fastvideo_args
+            decoder = self._stage_name_mapping["decoding_stage"]
+            decoded_frames, self._vae_cache = decoder.streaming_decode(
+                current_latents,
+                args,
+                cache=self._vae_cache,
+                is_first_chunk=(start_idx == 0))
+            batch.output = decoded_frames
+        else:
+            batch.output = None
+
+        return batch
+
+    def streaming_clear(self) -> None:
+        denoiser = self._stage_name_mapping.get("denoising_stage")
+        if denoiser is not None and hasattr(denoiser, "streaming_clear"):
+            denoiser.streaming_clear()
+        self._vae_cache = None
+
+EntryClass = [WanGameCausalDMDPipeline]
@@ -0,0 +1,78 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Wan video diffusion pipeline implementation.
+
+This module contains an implementation of the Wan video diffusion pipeline
+using the modular pipeline architecture.
+"""
+
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
+from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
+from fastvideo.pipelines.lora_pipeline import LoRAPipeline
+
+# isort: off
+from fastvideo.pipelines.stages import (
+    ImageEncodingStage, ConditioningStage, DecodingStage, DenoisingStage,
+    ImageVAEEncodingStage, InputValidationStage, LatentPreparationStage,
+    TimestepPreparationStage)
+# isort: on
+from fastvideo.models.schedulers.scheduling_flow_unipc_multistep import (
+    FlowUniPCMultistepScheduler)
+
+logger = init_logger(__name__)
+
+
+class WanGameActionImageToVideoPipeline(LoRAPipeline, ComposedPipelineBase):
+
+    _required_config_modules = [
+        "vae", "transformer", "scheduler", \
+        "image_encoder", "image_processor"
+    ]
+
+    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
+        self.modules["scheduler"] = FlowUniPCMultistepScheduler(
+            shift=fastvideo_args.pipeline_config.flow_shift)
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
+        """Set up pipeline stages with proper dependency injection."""
+
+        self.add_stage(stage_name="input_validation_stage",
+                       stage=InputValidationStage())
+
+        self.add_stage(
+            stage_name="image_encoding_stage",
+            stage=ImageEncodingStage(
+                image_encoder=self.get_module("image_encoder"),
+                image_processor=self.get_module("image_processor"),
+            ))
+
+        self.add_stage(stage_name="conditioning_stage",
+                       stage=ConditioningStage())
+
+        self.add_stage(stage_name="timestep_preparation_stage",
+                       stage=TimestepPreparationStage(
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer")))
+
+        self.add_stage(stage_name="image_latent_preparation_stage",
+                       stage=ImageVAEEncodingStage(vae=self.get_module("vae")))
+
+        self.add_stage(stage_name="denoising_stage",
+                       stage=DenoisingStage(
+                           transformer=self.get_module("transformer"),
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="decoding_stage",
+                       stage=DecodingStage(vae=self.get_module("vae")))
+
+
+class WanLingBotImageToVideoPipeline(WanGameActionImageToVideoPipeline):
+    pass
+
+
+EntryClass = [WanGameActionImageToVideoPipeline, WanLingBotImageToVideoPipeline]
@@ -15,8 +15,6 @@ import torch
 from fastvideo.configs.pipelines import PipelineConfig
 from fastvideo.distributed import (
    maybe_init_distributed_environment_and_model_parallel, get_world_group)
-from fastvideo.distributed.communication_op import (
-    warmup_sequence_parallel_communication)
 from fastvideo.fastvideo_args import FastVideoArgs, TrainingArgs
 from fastvideo.logger import init_logger
 from fastvideo.profiler import get_or_create_profiler
@@ -101,58 +99,6 @@ class ComposedPipelineBase(ABC):
                module.requires_grad_(True)
                module.train()

-    @staticmethod
-    def _compile_with_conditions(
-        module: torch.nn.Module,
-        compile_kwargs: dict[str, Any],
-    ) -> int:
-        """Compile submodules that match module._compile_conditions."""
-        compile_conditions = getattr(module, "_compile_conditions", None)
-        if not compile_conditions:
-            return 0
-
-        compiled_count = 0
-        for name, submodule in module.named_modules():
-            if not name:
-                continue
-            if any(cond(name, submodule) for cond in compile_conditions):
-                submodule.forward = torch.compile(submodule.forward,
-                                                  **compile_kwargs)
-                compiled_count += 1
-        return compiled_count
-
-    def _maybe_compile_pipeline_module(
-        self,
-        module_name: str,
-        fsdp_module_cls: type | None,
-        compile_kwargs: dict[str, Any],
-    ) -> None:
-        if module_name not in self.modules:
-            return
-
-        module = self.modules[module_name]
-        if fsdp_module_cls is not None and isinstance(module, fsdp_module_cls):
-            logger.info(
-                "%s is already FSDP-wrapped; skipping torch.compile in pipeline",
-                module_name.capitalize(),
-            )
-            return
-
-        compiled_count = self._compile_with_conditions(module, compile_kwargs)
-        if compiled_count > 0:
-            logger.info(
-                "Enabled torch.compile for %d submodules in %s via _compile_conditions with kwargs=%s",
-                compiled_count,
-                module_name,
-                compile_kwargs,
-            )
-            return
-
-        # Backward-compatible fallback: compile full module if no condition matched.
-        logger.info("Enabling torch.compile for %s with kwargs=%s", module_name,
-                    compile_kwargs)
-        self.modules[module_name] = torch.compile(module, **compile_kwargs)
-
    def post_init(self) -> None:
        assert self.fastvideo_args is not None, "fastvideo_args must be set"
        if self.post_init_called:
@@ -168,6 +114,7 @@ class ComposedPipelineBase(ABC):

        self.initialize_pipeline(self.fastvideo_args)
        if self.fastvideo_args.enable_torch_compile:
+            transformer_module = self.modules["transformer"]
            if self.fastvideo_args.training_mode:
                logger.info(
                    "Torch Compile enabled via FSDP loader for training; skipping additional pipeline compile"
@@ -181,26 +128,35 @@ class ComposedPipelineBase(ABC):
                    fsdp_module_cls = None

                compile_kwargs = self.fastvideo_args.torch_compile_kwargs or {}
-                self._maybe_compile_pipeline_module(
-                    module_name="transformer",
-                    fsdp_module_cls=fsdp_module_cls,
-                    compile_kwargs=compile_kwargs,
-                )
-                self._maybe_compile_pipeline_module(
-                    module_name="transformer_2",
-                    fsdp_module_cls=fsdp_module_cls,
-                    compile_kwargs=compile_kwargs,
-                )
+                if fsdp_module_cls is not None and isinstance(
+                        transformer_module, fsdp_module_cls):
+                    logger.info(
+                        "Transformer is already FSDP-wrapped; skipping torch.compile in pipeline"
+                    )
+                else:
+                    logger.info("Enabling torch.compile for DiT with kwargs=%s",
+                                compile_kwargs)
+                    self.modules["transformer"] = torch.compile(
+                        transformer_module, **compile_kwargs)
+                if "transformer_2" in self.modules:
+                    transformer_module_2 = self.modules["transformer_2"]
+                    if fsdp_module_cls is not None and isinstance(
+                            transformer_module_2, fsdp_module_cls):
+                        logger.info(
+                            "Transformer_2 is already FSDP-wrapped; skipping torch.compile in pipeline"
+                        )
+                    else:
+                        logger.info(
+                            "Enabling torch.compile for Transformer_2 with kwargs=%s",
+                            compile_kwargs)
+                        self.modules["transformer_2"] = torch.compile(
+                            transformer_module_2, **compile_kwargs)
                logger.info("Torch Compile enabled for DiT")

        if not self.fastvideo_args.training_mode:
            logger.info("Creating pipeline stages...")
            self.create_pipeline_stages(self.fastvideo_args)

-            # Warmup NCCL communicators for sequence parallelism to avoid
-            # slow first forward pass due to lazy initialization
-            warmup_sequence_parallel_communication()
-
    def initialize_training_pipeline(self, training_args: TrainingArgs):
        raise NotImplementedError(
            "if training_mode is True, the pipeline must implement this method")
@@ -331,7 +287,6 @@ class ComposedPipelineBase(ABC):
        # remove keys that are not pipeline modules
        model_index.pop("_class_name")
        model_index.pop("_diffusers_version")
-        model_index.pop("_name_or_path", None)
        model_index.pop("workload_type", None)
        if "boundary_ratio" in model_index and model_index[
                "boundary_ratio"] is not None:
@@ -123,7 +123,6 @@ class ForwardBatch:

    # Latent tensors
    latents: torch.Tensor | None = None
-    lq_latents: torch.Tensor | None = None
    raw_latent_shape: tuple[int, ...] | None = None
    noise_pred: torch.Tensor | None = None
    image_latent: torch.Tensor | None = None
@@ -145,8 +144,6 @@ class ForwardBatch:
    # Original dimensions (before VAE scaling)
    height: list[int] | int | None = None
    width: list[int] | int | None = None
-    height_sr: list[int] | int | None = None
-    width_sr: list[int] | int | None = None
    fps: list[int] | int | None = None

    # Timesteps
@@ -157,7 +154,6 @@ class ForwardBatch:

    # Scheduler parameters
    num_inference_steps: int = 50
-    num_inference_steps_sr: int = 50
    guidance_scale: float = 1.0
    guidance_scale_2: float | None = None
    guidance_rescale: float = 0.0
@@ -16,6 +16,31 @@ from fastvideo.pipelines.lora_pipeline import LoRAPipeline

 logger = init_logger(__name__)

+# map pipeline name to folder name
+_PIPELINE_NAME_TO_ARCHITECTURE_NAME: dict[str, str] = {
+    "WanPipeline": "wan",
+    "WanDMDPipeline": "wan",
+    "WanImageToVideoPipeline": "wan",
+    "WanVideoToVideoPipeline": "wan",
+    "WanCausalDMDPipeline": "wan",
+    "TurboDiffusionPipeline": "turbodiffusion",
+    "TurboDiffusionI2VPipeline": "turbodiffusion",
+    "StepVideoPipeline": "stepvideo",
+    "HunyuanVideoPipeline": "hunyuan",
+    "HunyuanVideo15Pipeline": "hunyuan15",
+    "HYWorldPipeline": "hyworld",
+    "Cosmos2VideoToWorldPipeline": "cosmos",
+    "Cosmos2_5Pipeline": "cosmos",
+    "MatrixGamePipeline": "matrixgame",
+    "MatrixGameCausalDMDPipeline": "matrixgame",
+    "WanGameActionImageToVideoPipeline": "wan",
+    "WanGameCausalDMDPipeline": "wan",
+    "LongCatPipeline": "longcat",
+    "LongCatImageToVideoPipeline": "longcat",
+    "LongCatVideoContinuationPipeline": "longcat",
+    "LTX2Pipeline": "ltx2",
+}
+
 _PREPROCESS_WORKLOAD_TYPE_TO_PIPELINE_NAME: dict[WorkloadType, str] = {
    WorkloadType.I2V: "PreprocessPipelineI2V",
    WorkloadType.T2V: "PreprocessPipelineT2V",
@@ -50,36 +75,44 @@ class PipelineType(str, Enum):

@dataclass
 class _PipelineRegistry:
-    # Keyed by pipeline_type -> pipeline_name
-    # pipelines[pipeline_type][pipeline_name] = pipeline_cls
-    pipelines: dict[str, dict[str, type[ComposedPipelineBase]
-                              | None]] = field(default_factory=dict)
+    # Keyed by pipeline_type -> architecture -> pipeline_name
+    # pipelines[pipeline_type][architecture][pipeline_name] = pipeline_cls
+    pipelines: dict[str, dict[str, dict[str, type[ComposedPipelineBase]
+                                        | None]]] = field(default_factory=dict)

-    def get_supported_pipelines(self, pipeline_type: PipelineType) -> Set[str]:
-        """Get supported pipelines for the given pipeline type."""
-        return set(self.pipelines.get(pipeline_type.value, {}).keys())
+    def get_supported_archs(self, pipeline_name_in_config: str,
+                            pipeline_type: PipelineType) -> Set[str]:
+        """Get supported architectures, optionally filtered by pipeline type and workload type."""
+        arch = _PIPELINE_NAME_TO_ARCHITECTURE_NAME[pipeline_name_in_config]
+        return set(self.pipelines[pipeline_type.value][arch].keys())

    def _load_preprocess_pipeline_cls(
-            self,
-            workload_type: WorkloadType) -> type[ComposedPipelineBase] | None:
+            self, workload_type: WorkloadType,
+            arch: str) -> type[ComposedPipelineBase] | None:
        pipeline_name = _PREPROCESS_WORKLOAD_TYPE_TO_PIPELINE_NAME[
            workload_type]
-        return self.pipelines.get(PipelineType.PREPROCESS.value,
-                                  {}).get(pipeline_name)
+
+        return self.pipelines[
+            PipelineType.PREPROCESS.value][arch][pipeline_name]

    def _try_load_pipeline_cls(
        self, pipeline_name_in_config: str, pipeline_type: PipelineType,
        workload_type: WorkloadType
    ) -> type[ComposedPipelineBase] | type[LoRAPipeline] | None:
        """Try to load a pipeline class for the given architecture, pipeline type, and workload type."""
-        if pipeline_type.value not in self.pipelines:
+        arch = _PIPELINE_NAME_TO_ARCHITECTURE_NAME[pipeline_name_in_config]
+
+        if (pipeline_type.value not in self.pipelines
+                or arch not in self.pipelines[pipeline_type.value]):
            return None

        if pipeline_type == PipelineType.PREPROCESS:
-            return self._load_preprocess_pipeline_cls(workload_type)
-        elif pipeline_type == PipelineType.BASIC or pipeline_type == PipelineType.TRAINING:
-            return self.pipelines[pipeline_type.value].get(
-                pipeline_name_in_config)
+            return self._load_preprocess_pipeline_cls(workload_type, arch)
+        elif pipeline_type == PipelineType.BASIC:
+            return self.pipelines[
+                pipeline_type.value][arch][pipeline_name_in_config]
+        elif pipeline_type == PipelineType.TRAINING:
+            pass
        else:
            raise ValueError(f"Invalid pipeline type: {pipeline_type.value}")

@@ -99,28 +132,18 @@ class _PipelineRegistry:
                                                   pipeline_type, workload_type)
        if pipeline_cls is not None:
            return pipeline_cls
-        supported_pipelines = self.get_supported_pipelines(pipeline_type)
+        supported_archs = self.get_supported_archs(pipeline_name_in_config,
+                                                   pipeline_type)
        raise ValueError(
-            f"Pipeline '{pipeline_name_in_config}' is not supported for pipeline type '{pipeline_type.value}' "
+            f"Pipeline architecture '{pipeline_name_in_config}' is not supported for pipeline type '{pipeline_type.value}' "
            f"and workload type '{workload_type.value}'. "
-            f"Supported pipelines: {supported_pipelines}")
-
-
-def import_pipeline_classes(
-    pipeline_types: list[PipelineType] | PipelineType | None = None
-) -> dict[str, dict[str, type[ComposedPipelineBase] | None]]:
-    pipeline_types_key: tuple[PipelineType, ...] | PipelineType | None
-    if isinstance(pipeline_types, list):
-        pipeline_types_key = tuple(pipeline_types)
-    else:
-        pipeline_types_key = pipeline_types
-    return _import_pipeline_classes_cached(pipeline_types_key)
+            f"Supported architectures: {supported_archs}")


@lru_cache
-def _import_pipeline_classes_cached(
-    pipeline_types: tuple[PipelineType, ...] | PipelineType | None = None
-) -> dict[str, dict[str, type[ComposedPipelineBase] | None]]:
+def import_pipeline_classes(
+    pipeline_types: list[PipelineType] | PipelineType | None = None
+) -> dict[str, dict[str, dict[str, type[ComposedPipelineBase] | None]]]:
    """
    Import pipeline classes based on the pipeline type and workload type.
    
@@ -129,16 +152,19 @@ def _import_pipeline_classes_cached(
                      If None, loads all types.
    
    Returns:
-        A two-level nested dictionary:
-        {pipeline_type: {pipeline_name: pipeline_cls}}
-        e.g., {"basic": {"WanPipeline": WanPipeline}}
+        A three-level nested dictionary:
+        {pipeline_type: {architecture_name: {pipeline_name: pipeline_cls}}}
+        e.g., {"basic": {"wan": {"WanPipeline": WanPipeline}}}
    """
-    type_to_pipeline_dict: dict[str, dict[str, type[ComposedPipelineBase]
-                                          | None]] = {}
+    type_to_arch_to_pipeline_dict: dict[str,
+                                        dict[str,
+                                             dict[str,
+                                                  type[ComposedPipelineBase]
+                                                  | None]]] = {}
    package_name: str = "fastvideo.pipelines"

    # Determine which pipeline types to scan
-    if isinstance(pipeline_types, tuple):
+    if isinstance(pipeline_types, list):
        pipeline_types_to_scan = [
            pipeline_type.value for pipeline_type in pipeline_types
        ]
@@ -150,7 +176,8 @@ def _import_pipeline_classes_cached(
    logger.info("Loading pipelines for types: %s", pipeline_types_to_scan)

    for pipeline_type_str in pipeline_types_to_scan:
-        pipeline_dict: dict[str, type[ComposedPipelineBase] | None] = {}
+        arch_to_pipeline_dict: dict[str, dict[str, type[ComposedPipelineBase]
+                                              | None]] = {}

        # Try to load from pipeline-type-specific directory first
        pipeline_type_package_name = f"{package_name}.{pipeline_type_str}"
@@ -162,45 +189,50 @@ def _import_pipeline_classes_cached(

            for _, arch, ispkg in pkgutil.iter_modules(
                    pipeline_type_package.__path__):
+                pipeline_dict: dict[str, type[ComposedPipelineBase] | None] = {}
+
                arch_package_name = f"{pipeline_type_package_name}.{arch}"
-                if not ispkg:
-                    continue
+                if ispkg:
+                    arch_package = importlib.import_module(arch_package_name)
+                    for _, module_name, ispkg in pkgutil.walk_packages(
+                            arch_package.__path__, arch_package_name + "."):
+                        if not ispkg:
+                            pipeline_module = importlib.import_module(
+                                module_name)
+                            if hasattr(pipeline_module, "EntryClass"):
+                                if isinstance(pipeline_module.EntryClass, list):
+                                    for pipeline in pipeline_module.EntryClass:
+                                        pipeline_name = pipeline.__name__
+                                        assert (
+                                            pipeline_name not in pipeline_dict
+                                        ), f"Duplicated pipeline implementation for {pipeline_name} in {pipeline_type_str}.{arch_package_name}"
+                                        pipeline_dict[pipeline_name] = pipeline
+                                else:
+                                    pipeline_name = pipeline_module.EntryClass.__name__
+                                    assert (
+                                        pipeline_name not in pipeline_dict
+                                    ), f"Duplicated pipeline implementation for {pipeline_name} in {pipeline_type_str}.{arch_package_name}"
+                                    pipeline_dict[
+                                        pipeline_name] = pipeline_module.EntryClass

-                arch_package = importlib.import_module(arch_package_name)
-                for _, module_name, ispkg in pkgutil.walk_packages(
-                        arch_package.__path__, arch_package_name + "."):
-                    if ispkg:
-                        continue
-                    pipeline_module = importlib.import_module(module_name)
-                    if not hasattr(pipeline_module, "EntryClass"):
-                        continue
-                    entry_cls = pipeline_module.EntryClass
-                    entry_cls_list = ([
-                        entry_cls
-                    ] if not isinstance(entry_cls, list) else entry_cls)
-
-                    for pipeline in entry_cls_list:
-                        pipeline_name = pipeline.__name__
-                        if pipeline_name in pipeline_dict:
-                            logger.warning(
-                                "Duplicate pipeline name '%s' found in %s. Overwriting.",
-                                pipeline_name, pipeline_type_str)
-                        pipeline_dict[pipeline_name] = pipeline
+                arch_to_pipeline_dict[arch] = pipeline_dict

        except ImportError as e:
            raise ImportError(
                f"Could not import {pipeline_type_package_name} when importing pipeline classes: {e}"
            ) from None

-        type_to_pipeline_dict[pipeline_type_str] = pipeline_dict
+        type_to_arch_to_pipeline_dict[pipeline_type_str] = arch_to_pipeline_dict

    # Log summary
    total_pipelines = sum(
-        len(pipeline_dict) for pipeline_dict in type_to_pipeline_dict.values())
+        len(pipeline_dict)
+        for arch_to_pipeline_dict in type_to_arch_to_pipeline_dict.values()
+        for pipeline_dict in arch_to_pipeline_dict.values())
    logger.info("Loaded %d pipeline classes across %d types", total_pipelines,
                len(pipeline_types_to_scan))

-    return type_to_pipeline_dict
+    return type_to_arch_to_pipeline_dict


 def get_pipeline_registry(
@@ -10,11 +10,10 @@ from fastvideo.pipelines.stages.base import PipelineStage
 from fastvideo.pipelines.stages.causal_denoising import CausalDMDDenosingStage
 from fastvideo.pipelines.stages.conditioning import ConditioningStage
 from fastvideo.pipelines.stages.decoding import DecodingStage
-from fastvideo.pipelines.stages.denoising import (
-    Cosmos25AutoDenoisingStage, Cosmos25DenoisingStage,
-    Cosmos25V2WDenoisingStage, Cosmos25T2WDenoisingStage, CosmosDenoisingStage,
-    DenoisingStage, DmdDenoisingStage)
-from fastvideo.pipelines.stages.sr_denoising import SRDenoisingStage
+from fastvideo.pipelines.stages.denoising import (Cosmos25DenoisingStage,
+                                                  CosmosDenoisingStage,
+                                                  DenoisingStage,
+                                                  DmdDenoisingStage)
 from fastvideo.pipelines.stages.encoding import EncodingStage
 from fastvideo.pipelines.stages.image_encoding import (
    ImageEncodingStage, MatrixGameImageEncodingStage, RefImageEncodingStage,
@@ -23,13 +22,11 @@ from fastvideo.pipelines.stages.image_encoding import (
 from fastvideo.pipelines.stages.input_validation import InputValidationStage
 from fastvideo.pipelines.stages.latent_preparation import (
    Cosmos25LatentPreparationStage, CosmosLatentPreparationStage,
-    Cosmos25AutoLatentPreparationStage, Cosmos25T2WLatentPreparationStage,
-    Cosmos25V2WLatentPreparationStage, LatentPreparationStage)
+    LatentPreparationStage)
 from fastvideo.pipelines.stages.ltx2_audio_decoding import LTX2AudioDecodingStage
 from fastvideo.pipelines.stages.ltx2_denoising import LTX2DenoisingStage
 from fastvideo.pipelines.stages.ltx2_latent_preparation import (
    LTX2LatentPreparationStage)
-from fastvideo.pipelines.stages.ltx2_text_encoding import LTX2TextEncodingStage
 from fastvideo.pipelines.stages.matrixgame_denoising import (
    MatrixGameCausalDenoisingStage)
 from fastvideo.pipelines.stages.hyworld_denoising import HYWorldDenoisingStage
@@ -53,9 +50,6 @@ __all__ = [
    "LatentPreparationStage",
    "CosmosLatentPreparationStage",
    "Cosmos25LatentPreparationStage",
-    "Cosmos25T2WLatentPreparationStage",
-    "Cosmos25V2WLatentPreparationStage",
-    "Cosmos25AutoLatentPreparationStage",
    "LTX2LatentPreparationStage",
    "LTX2AudioDecodingStage",
    "ConditioningStage",
@@ -66,12 +60,7 @@ __all__ = [
    "HYWorldDenoisingStage",
    "CosmosDenoisingStage",
    "Cosmos25DenoisingStage",
-    "Cosmos25T2WDenoisingStage",
-    "Cosmos25V2WDenoisingStage",
-    "Cosmos25AutoDenoisingStage",
    "LTX2DenoisingStage",
-    "LTX2TextEncodingStage",
-    "SRDenoisingStage",
    "EncodingStage",
    "DecodingStage",
    "ImageEncodingStage",
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
kevin314	0c606b7cfa	Add model selection	2026-02-14 02:06:09 +00:00
kevin314	0a0cdebaba	Add gpu selection	2026-02-05 04:02:49 +00:00
kevin314	cd9e7a8cac	Add gpu provisioning	2026-02-04 07:39:31 +00:00
kevin314	87e19dad5a	Add model resetting	2026-02-02 21:36:52 +00:00
kevin314	79195ad18e	Add web app for matrix game	2026-02-01 00:12:07 +00:00